JP7669317B2 - Information processing device, method and program - Google Patents

Description

This disclosure relates to technology for assisting users in performing operations.

Conventionally, call center operators make phone calls to customers to urge them to make credit card payments, i.e., to collect debts (see Patent Document 1). Also known is a technology for supporting urging customers to make payments by changing the tone of the sample phrases that operators use when responding to calls depending on the time of day that urging work is performed (see Patent Document 2).

JP 2001-282994 A JP 2010-224617 A

Conventionally, operations (e.g., calling a customer, etc.) have been performed to prompt a user to perform a specific action (e.g., transferring money or depositing funds into an account) in order to have the user perform the specific action. However, while operations that prompt a user to perform a specific action are effective, there is a problem in that performing the operation incurs costs that correspond to the amount of the operation.

In view of the above problems, the present disclosure aims to improve the cost-effectiveness of operations that prompt users to take specific actions.

An example of the present disclosure is an information processing device that includes an effect estimation means for estimating an effect that a predetermined operation for a user to prompt the user to perform a predetermined action has on whether or not the user performs the action, a reference user identification means for identifying a reference user who is related to a target user, and a condition output means for outputting a condition regarding the operation for the target user based on data including a target effect, which is the effect estimated for the target user, and a reference effect, which is the effect estimated for the reference user.

The present disclosure can be understood as an information processing device, a system, a method executed by a computer, or a program executed by a computer. The present disclosure can also be understood as such a program recorded on a recording medium readable by a computer or other device, machine, etc. Here, a recording medium readable by a computer, etc. refers to a recording medium that stores information such as data and programs through electrical, magnetic, optical, mechanical, or chemical action and can be read by a computer, etc.

This disclosure makes it possible to improve the cost-effectiveness of operations that encourage users to take specific actions.

1 is a schematic diagram showing a configuration of an information processing system according to an embodiment. 1 is a diagram illustrating an outline of a functional configuration of an information processing device according to an embodiment. FIG. 10 is a diagram illustrating an example in which IP address data values are common in the embodiment. FIG. 4 is a diagram illustrating an example of graph data according to the embodiment. FIG. 10 is a diagram illustrating an example in which address data values are common in the embodiment. FIG. 4 is a diagram illustrating an example of graph data according to the embodiment. FIG. 10 is a diagram illustrating an example in which the value of credit card number data is common in the embodiment. FIG. 4 is a diagram illustrating an example of graph data according to the embodiment. FIG. 4 is a diagram illustrating an example of graph data according to the embodiment. FIG. 2 is a diagram illustrating an example of a cluster according to an embodiment. FIG. 13 is a diagram illustrating an example of visualization of classification according to the embodiment. FIG. 1 illustrates an example of determining relationship strength (closeness score) using a machine learning model according to an embodiment. FIG. 2 is a diagram illustrating a simplified concept of a decision tree of a machine learning model employed in an embodiment. FIG. 13 is a diagram showing the relationship between the effects and risks estimated in the embodiment and the operation conditions. 1 is a flowchart illustrating a flow of a machine learning process according to an embodiment. 13 is a flowchart showing the flow of an operation condition output process according to the embodiment. 13 is a flowchart showing a flow of variation (1) of an operation condition output process according to the embodiment. 13 is a flowchart showing a flow of variation (2) of an operation condition output process according to the embodiment. A diagram showing the relationship between the estimated target causal score, reference causal score, and risk index and operation conditions according to the embodiment. A diagram showing the relationship between the estimated target causal score, reference causal score, and risk index and operation conditions according to the embodiment. 13 is a flowchart showing the flow of an operation condition output process in a case where the relationships between the target causal score, the reference causal score, and the risk index and the operation condition according to the embodiment are adopted. 13 is a flowchart showing the flow of an operation condition output process in a case where the relationships between the target causal score, the reference causal score, and the risk index and the operation condition according to the embodiment are adopted.

Below, an embodiment of an information processing device, method, and program according to the present disclosure will be described with reference to the drawings. However, the embodiment described below is merely an example of an embodiment, and the information processing device, method, and program according to the present disclosure are not limited to the specific configuration described below. In implementing the present disclosure, a specific configuration according to the embodiment may be appropriately adopted, and various improvements and modifications may be made.

In this embodiment, an embodiment will be described in which the technology according to the present disclosure is implemented for an operations center management system for collecting receivables by urging payment of overdue credit card usage amounts. However, systems to which the technology according to the present disclosure can be applied are not limited to operations center management systems for urging payment of credit card usage amounts. The technology according to the present disclosure can be widely used as a technology for supporting operations for users, and the application of the present disclosure is not limited to the examples shown in the embodiments.

Normally, credit card usage fees are paid by debiting the user's account on the monthly withdrawal date or by the user depositing the amount by a specified date, but there are cases where the payment of the credit card usage fee is not completed by the specified date due to reasons such as insufficient funds in the user's account or the user not depositing the amount by the specified date. For this reason, in the past, operations such as calling customers or sending messages from an operations center (call center) were used to urge users to pay their credit card usage fees and collect receivables.

In general, operations performed on users are effective in avoiding defaults (non-performance of debt), and the more operations performed, the higher the debt recovery rate. However, on the other hand, the more operations performed, the higher the costs of operations, such as labor costs, system usage fees, and system maintenance costs. In view of the above problems, the system disclosed herein employs technology to reduce costs related to operations without lowering the debt recovery rate. Note that in this embodiment, an example will be described in which the specified operation is mainly a phone call to the user, but the content of the specified operation is not limited, and may be various operations to encourage the user to take a specified action.

<System Configuration>
1 is a schematic diagram showing the configuration of an information processing system according to this embodiment. In the system according to this embodiment, an information processing device 1, an operation center management system 3, and a credit card management system 5 are connected to each other so that they can communicate with each other. An operation terminal (not shown) is installed in the operation center for performing operations according to instructions from the operation center management system 3, and an operator operates the operation terminal to perform operations for a user. The user is a credit card user who deposits the credit card usage amount via a financial institution or the like, and payment history data of the credit card usage amount is notified to the operation center management system 3 via the credit card management system 5.

The information processing device 1 is an information processing device for outputting data for controlling operations by the operation center management system 3. The information processing device 1 is a computer equipped with a CPU (Central Processing Unit) 11, a ROM (Read Only Memory) 12, a RAM (Random Access Memory) 13, a storage device 14 such as an EEPROM (Electrically Erasable and Programmable Read Only Memory) or a HDD (Hard Disk Drive), a communication unit 15 such as a NIC (Network Interface Card), etc. However, the specific hardware configuration of the information processing device 1 can be omitted, replaced, or added as appropriate depending on the embodiment. Furthermore, the information processing device 1 is not limited to a device consisting of a single housing. The information processing device 1 may be realized by multiple devices using so-called cloud or distributed computing technology, etc.

The operation center management system 3, the credit card management system 5, and the operation terminal are all computers equipped with a CPU, ROM, RAM, a storage device, a communication unit, an input device, an output device, etc. (not shown). Furthermore, these systems and terminals are not limited to devices consisting of a single housing. These systems and terminals may be realized by multiple devices using so-called cloud or distributed computing technology, etc.

FIG. 2 is a diagram showing an outline of the functional configuration of the information processing device 1 according to this embodiment. The information processing device 1 functions as an information processing device including a graph data generating unit 21, a reference user identifying unit 22, a relationship identifying unit 23, a relationship strength determining unit 24, an effect estimating unit 25, a risk estimating unit 26, a machine learning unit 27, and a condition output unit 28, by a program recorded in the storage device 14 being read into the RAM 13 and executed by the CPU 11, which controls each piece of hardware included in the information processing device 1. Note that in this embodiment and other embodiments described below, each function included in the information processing device 1 is executed by the CPU 11, which is a general-purpose processor, but some or all of these functions may be executed by one or more dedicated processors.

The graph data generating unit 21 generates graph data (social graph network) showing the relationships between users by identifying pairs of users who are related to each other based on the attribute data groups of each of the users. More specifically, the graph data generating unit 21 generates graph data including, for example, node data 50 corresponding to each of multiple users including the target user, and link data 52 corresponding to pairs of users who are related to each other (see Figures 4, 6, 8, and 9). The graph data generating unit 21 predicts and creates implicit links between users by learning (representation learning, relationship learning, embedding learning, knowledge graph embedding) a user relationship graph consisting of nodes (users) connected by explicit links. At this time, the graph data generating unit 21 may perform the learning based on a known embedding model or an extension thereof as appropriate.

For example, as shown in FIG. 3, suppose that a group of attribute data for user A is registered in electronic commerce system 40. Also, suppose that a group of attribute data for user B is registered in golf course reservation system 42. Also, suppose that attribute data for user C is registered in travel reservation system 44. And suppose that the value of the IP address data for user A registered in electronic commerce system 40, the value of the IP address data for user B registered in golf course reservation system 42, and the value of the IP address data for user C registered in travel reservation system 44 are the same.

In this case, as shown in FIG. 4, the graph data generation unit 21 generates graph data including node data 50a associated with user A, node data 50b associated with user B, node data 50c associated with user C, link data 52a indicating that user A is related to user B, link data 52b indicating that user A is related to user C, and link data 52c indicating that user B is related to user C. It is presumed that users with the same IP address use the same computer or share a global address in the same residence or workplace. For this reason, in this embodiment, such users are associated with each other.

For example, suppose that attribute data groups for user D, user E, and user F are registered in electronic commerce system 40, as shown in FIG. 5. Also, suppose that the address data values of user D, user E, and user F registered in electronic commerce system 40 are the same.

In this case, as shown in FIG. 6, the graph data generation unit 21 generates graph data including node data 50d associated with user D, node data 50e associated with user E, node data 50f associated with user F, link data 52d indicating that user D is related to user E, link data 52e indicating that user D is related to user F, and link data 52f indicating that user E is related to user F. It is presumed that users with the same address live together. Therefore, in this embodiment, such users are associated with each other.

For example, as shown in FIG. 7, suppose that a group of attribute data for user G is registered in electronic commerce system 40. Also, suppose that a group of attribute data for user H is registered in golf course reservation system 42. Also, suppose that a group of attribute data for user I is registered in travel reservation system 44. And suppose that the value of the credit card number data for user G registered in electronic commerce system 40, the value of the credit card number data for user H registered in golf course reservation system 42, and the value of the credit card number data for user I registered in travel reservation system 44 are the same.

In this case, as shown in FIG. 8, the graph data generation unit 21 generates graph data including node data 50g associated with user G, node data 50h associated with user H, node data 50i associated with user I, link data 52g indicating that user G is related to user H, link data 52h indicating that user G is related to user I, and link data 52i indicating that user H is related to user I. Users with the same credit card number are presumed to be family members, such as parents and children. For this reason, in this embodiment, such users are associated with each other.

The criteria for determining whether a pair of users is related to each other are not limited to those described above. User pairs can be determined based on various criteria, such as location history and behavior history.

The link indicated by the link data 52 associating users identified as having a relationship with each other as described above is referred to as an explicit link. For example, a user connected to a first user via an explicit link and a user connected to a second user via an explicit link have a predetermined number of users in common (e.g., three or more). In this case, in this embodiment, for example, the graph data generation unit 21 generates link data 52 indicating that the first user is related to the second user. The link indicated by the link data 52 generated in this way is referred to as an implicit link.

For example, as shown in FIG. 9, it is assumed that node data 50j associated with user J and node data 50k associated with user K are connected by link data 52j indicating an explicit link. It is also assumed that node data 50j associated with user J and node data 50l associated with user L are connected by link data 52k indicating an explicit link. It is also assumed that node data 50j associated with user J and node data 50m associated with user M are connected by link data 52l indicating an explicit link.

In addition, it is assumed that the node data 50k associated with user K and the node data 50n associated with user N are connected by link data 52m indicating an explicit link. It is assumed that the node data 50l associated with user L and the node data 50n associated with user N are connected by link data 52n indicating an explicit link. It is assumed that the node data 50m associated with user M and the node data 50n associated with user N are connected by link data 52o indicating an explicit link.

In this case, the graph data generating unit 21 generates link data 52p (link data 52p indicating an implicit link) indicating that user J is related to user N. In this way, user N is identified as a user who is related to user J.

For example, suppose that a user connected to a first user via an explicit link or an implicit link has a predetermined number of users (e.g., three or more) in common with a second user connected to the first user via an explicit link or an implicit link. In this case, the graph data generating unit 21 may generate link data 52 indicating that the first user is related to the second user (link data 52 indicating an implicit link).

The reference user identification unit 22 refers to the graph data generated by the graph data generation unit 21, and identifies at least one other user who is related to the target user among the users included in the graph data as a reference user for the target user. Here, the reference user identification unit 22 may identify at least one reference user who is related to the target user according to the relationship strength (closeness score between users) described later. Specifically, the reference user identification unit 22 can preferentially identify users who have a strong relationship strength (high closeness score) with the target user as reference users. For example, when it is desired to identify n reference users, the reference user identification unit 22 identifies the top n users who have the strongest relationship strength with the target user as reference users. In addition, the reference user identification unit 22 may identify users who are identified as users who are related to the target user and users who have a predetermined number or more in common with the target user who are identified as users who are related to the target user as reference users. In addition, the reference user identification unit 22 may identify users who are classified into the same cluster as the target user as reference users.

The reference user identification unit 22 may also identify a reference user from among the multiple users based on the attributes of the target user and the attributes of the multiple users. For example, it can be assumed that other users who share an address or telephone number with the target user are cohabiting users who live with the target user, or users who share a relationship of living and social activities with the target user even if they have different addresses. For this reason, the reference user identification unit 22 can identify other users who share attribute data of addresses and telephone numbers with the target user as at least one reference user. The reference user identification unit 22 may also identify a user whose flag in common with the target user is enabled (set to 1) as a reference user. The reference user identification unit 22 may also identify a user whose various data estimated or determined based on the attribute data group indicates a predetermined value or a predetermined combination, etc., as a reference user. The reference user identification unit 22 may identify a reference user based on at least one of the conditions for identifying a reference user.

The reference user identification unit 22 may, for example, identify a user associated with node data 50 associated with a target user and node data 50 connected by link data 52 indicating an explicit link or an implicit link as a reference user for the target user.

The relationship identification unit 23 identifies the relationship between users. The relationship between users identified here may be, for example, (1) a parent-child relationship or a husband-wife relationship living in the same household, (2) a friendship relationship, (3) a relationship working in the same workplace, etc. However, the identified relationship is not limited to the examples in this disclosure. In this embodiment, the relationship identification unit 23 identifies the relationship between users based on the results of clustering based on values associated with the relationship between users. Here, the types of values that can be adopted as values associated with the relationship between users are not limited, but may include, for example, at least one of the user's name, IP address, address, credit card number, age, gender, place of school, place of work, and place of stay.

The relationship identification unit 23 identifies the relationship between users. Here, the relationship identification unit 23 may identify the relationship between the target user and the reference user based on the attribute data group of the target user and the attribute data group of the reference user. Furthermore, the computer system in which the attribute data group of the target user is registered may be different from the computer system in which the attribute data group of the reference user is registered. For example, the relationship between the target user and the reference user may be identified based on the attribute data group of the target user registered in the electronic commerce system 40 and the attribute data group of the reference user registered in the golf course reservation system 42.

The relationship identification unit 23, for example, identifies pairs of node data 50 that are connected by link data 52. Then, the relationship identification unit 23 generates pair attribute data associated with the pair based on a group of user attribute data of the two users associated with the pair. Here, the pair attribute data includes, for example, a common IP flag, a common address flag, a common credit card number flag, a same last name flag, age difference data, pair gender data, a common school flag, a common workplace flag, a common place of stay flag, etc.

The IP common flag is, for example, a flag indicating whether the value of the IP address data included in one attribute data of the pair is the same as the value of the IP address data included in the other attribute data. For example, if the values of the IP address data are the same, the value of the IP common flag may be set to 1, and if the values of the IP address data are different, the value of the IP common flag may be set to 0.

The common address flag, common school flag, common workplace flag, and common place of stay flag are flags that indicate, for example, whether the value of the address data, common school data, workplace data, or place of stay data included in one attribute data group of the pair is the same as the value of the address data, common school data, workplace data, or place of stay data included in the other attribute data group. For example, if the address data values are the same, the value of the common address flag may be set to 1, and if the address data values are different, the value of the common address flag may be set to 0.

The credit card number common flag is, for example, a flag indicating whether the value of the credit card number data included in one attribute data group of the pair is the same as the value of the credit card number data included in the other attribute data group. For example, if the values of the credit card number data are the same, the value of the credit card number common flag may be set to 1, and if the values of the credit card number data are different, the value of the credit card number common flag may be set to 0.

The same surname flag is, for example, a flag indicating whether the surname indicated by the name data included in one attribute data group of the pair is the same as the surname indicated by the name data included in the other attribute data group. For example, if the surnames indicated by the name data are the same, the value of the same surname flag may be set to 1, and if the surnames indicated by the name data are different, the value of the same surname flag may be set to 0.

The age difference data is, for example, data indicating the difference between the value of the age data included in one attribute data group of the pair and the value of the age data included in the other attribute data group.

Paired gender data is, for example, data that indicates a combination of the gender data value contained in one attribute data group of the pair and the gender data value contained in the other attribute data group.

Then, the relationship identification unit 23 performs clustering using a general clustering method based on the values of the pair attribute data group associated with each of the multiple pairs, thereby classifying the multiple pairs into multiple clusters 54 as shown in FIG. 10.

Figure 10 is a diagram showing a schematic example of how a plurality of pairs are classified into five clusters 54 (54a, 54b, 54c, 54d, and 54e). The crosses shown in Figure 10 correspond to pairs. Each of the plurality of crosses is placed at a position that corresponds to the value of the pair attribute data of the pair corresponding to the cross. In the example of Figure 10, the plurality of pairs are classified into five clusters 54, but the number of clusters 54 into which the plurality of pairs are classified is not limited to five, and for example, the plurality of pairs may be classified into four clusters 54.

FIG. 11 is a diagram showing an example of visualization of classification when multiple pairs are classified into four clusters 54. As shown in FIG. 11, pairs with the same address, the same gender, an age difference of more than X years, and the same last name may be classified into a first cluster. Pairs with the same address, the same gender, an age difference of X years or less, and the same last name may be classified into a second cluster. Pairs with the same address, different gender, an age difference of more than Y years, and the same last name may be classified into a third cluster. Pairs with the same address, different gender, an age difference of Y years or less, and the same last name may be classified into a fourth cluster.

In this case, the first cluster is presumed to be cluster 54 associated with, for example, parents and children of the same sex. The second cluster is presumed to be cluster 54 associated with siblings of the same sex. The third cluster is presumed to be cluster 54 associated with parents and children of the opposite sex. The fourth cluster is presumed to be cluster 54 associated with a married couple.

As described above, the relationship identification unit 23 may identify the relationship between the target user and the reference user based on the results of clustering based on values associated with the relationships between users. A specific example of creating clusters of friendships or working in the same workplace by clustering based on the common school flag, common workplace flag, and common place of stay flag is roughly similar to the example described above, so an explanation is omitted. In addition, the relationship identification unit 23 may identify the relationship between the target user and the reference user based on the results of clustering based on at least one of the family name, IP address, address, credit card number, age difference, gender, school, place of work, and place of stay.

The relationship strength determination unit 24 determines the relationship strength (hereinafter also referred to as "closeness score") indicating the closeness between the users (e.g., between the target user and the reference user) based on an index indicating the strength of the relationship between the users in accordance with a judgment criterion corresponding to the relationship between the users. In this embodiment, the relationship strength determination unit 24 determines the relationship strength (closeness score) indicating the closeness between the target user and the reference user based on the output when data indicating the index is input into a trained machine learning model corresponding to the relationship between the target user and the reference user.

Here, the relationship strength determination unit 24 may include a trained machine learning model that is associated with each of the above-mentioned clusters 54. For example, if multiple pairs are classified into five clusters 54, the relationship strength determination unit 24 may include five machine learning models. Then, the relationship strength determination unit 24 may determine a closeness score indicating the closeness between the target user and the reference user based on the output when data indicating an index indicating the strength of the relationship between the target user and the reference user is input to a trained machine learning model corresponding to the relationship between the target user and the reference user. In this case, the input/output relationship implemented in the trained machine learning model corresponds to the above-mentioned judgment criterion.

As shown in FIG. 12, the relationship strength determination unit 24 may input input data corresponding to a pair classified into a cluster 54 associated with the nth machine learning model to the nth machine learning model, which is the nth machine learning model. For example, if the relationship strength determination unit 24 includes five machine learning models, the above-mentioned value n is any integer between 1 and 5. Then, the relationship strength determination unit 24 may determine the value of the output data output from the nth machine learning model in response to the input of the input data as the value of the closeness score for the pair.

The input data associated with a pair may include, for example, some or all of the pair attribute data associated with the pair. The input data may also include data that is not included in the pair attribute data. For example, the input data may include data indicating the usage history of the electronic commerce system 40, data acquired by the relationship strength determination unit 24 from other information sources such as SNS, and the like. More specifically, for example, the input data may include data indicating the number of calls or message exchanges between the pair per unit period, the number of gifts sent by one to the other, the number of mutual friends in the pair, and the like.

Furthermore, the type of data included in the input data associated with a pair may be the same or different depending on the cluster 54 to which the pair belongs. For example, the type of data included in the input data input to the first machine learning model may be different from the type of data included in the input data input to the second machine learning model.

In this embodiment, for example, prior to the determination of the closeness score by the relationship strength determination unit 24, learning of the nth machine learning model is performed in advance using a given number of training data items associated with the nth machine learning model. This training data is, for example, prepared in advance so that the determination of the closeness score in the cluster 54 associated with the nth machine learning model is valid. Here, the closeness score set in the training data may be a rule-based set (annotated) closeness score. It may also be a closeness score that was previously output by the machine learning model and then modified by an administrator, etc.

Here, weakly supervised learning may be performed on the nth machine learning model. For example, the training data may include training input data that includes the same type of data as the input data input to the nth machine learning model, and training data that is compared with output data output from the nth machine learning model in response to the input of the training input data.

Here, for example, the closeness score described above takes a value of either 0 or 1. For example, if the pair is closely related, the closeness score value of the pair is determined to be 1, and if not, the closeness score value of the pair is determined to be 0. In this case, the teacher data may include a valid closeness score value for the corresponding learning input data and data indicating the probability that this value is valid. Then, for example, weakly supervised learning may be performed to update the parameter values of the nth machine learning model based on the value of the output data output from the nth machine learning model in response to the input of the learning input data included in the teacher data and the value of the teacher data included in the teacher data.

The above-mentioned closeness score does not have to be binary data that takes the value of either 0 or 1. For example, the above-mentioned closeness score may be a real number (e.g., a real number between 0 and 10 inclusive) that increases the closer the pair is to each other, or a multi-level integer value (e.g., an integer number between 1 and 10 inclusive).

Furthermore, the learning method of the machine learning model is not limited to weakly supervised learning. As a specific example, consider a pair that has a sibling relationship. In this case, input data associated with the pair is input to a trained machine learning model that corresponds to the sibling relationship. For example, if the address data values for the pair are the same, one of the pair has sent 50 gifts to the other, and the number of calls between the pair has been 1200, learning may be performed to output output data with a value of 1. For example, if the address data values for the pair are different, one of the pair has sent 2 gifts to the other, and the number of calls between the pair has been 30, learning may be performed to output output data with a value of 0. The criteria (e.g., threshold) for determining whether the value of the output data corresponding to the closeness score is 1 or 0 may differ depending on the machine learning model.

The effect estimation unit 25 estimates the effect of a predetermined operation for a user to prompt the user to perform a predetermined action on whether or not the user performs the action. In this embodiment, the predetermined action is payment of a credit card usage amount for which payment is delayed. The specific payment method is not limited, and may be a transfer to a designated account or payment at a designated counter. In this embodiment, the predetermined operation is a call to remind the user to pay the credit card usage amount for which payment is delayed. The call to the user may be an automated call using a recording or a machine voice, or a call in which an operator (human) talks to the user. In this embodiment, the effect estimation unit 25 estimates the effect of the operation using a machine learning model (hereinafter referred to as the "effect estimation model") that outputs a causality score indicating the effect of the operation on the user in response to input of one or more user attributes related to the target user.

However, some users may lack user attribute data for estimating the effect of an operation, and for such users, there is a problem in that it is difficult to estimate the effect of an operation based only on the user attributes of the user. For this reason, the effect estimation unit 25 according to the present disclosure estimates the effect of an operation on whether or not a target user performs an action for a user who lacks user attribute data for estimating the effect of an operation, by using the effect of the operation on whether or not other users (reference users) related to the user (target user) perform the action.

In the present disclosure, the effect that an operation has on whether or not a target user performs an action (target effect) is referred to as a "target causal score," and the effect that an operation has on whether or not a reference user performs an action (reference effect) is referred to as a "reference causal score." In the present embodiment, the effect estimation unit 25 estimates the target causal score, which is the effect on the target user, based on data including the reference causal score, which is the effect estimated on the reference user. Note that in the present disclosure, the target causal score or the reference causal score may simply be referred to as a causal score, or may simply be referred to as an effect (effect of an operation).

This is because, if there is a high probability that the reference user receives an operation such as a call to the target user on behalf of the target user, the effect of the operation on the target user may be affected by the effect of the operation on the reference user. For this reason, the reference user may be, for example, another user who has a certain relationship with the target user and lives with the target user. Also, for example, if the target user is an adult, it can be assumed that users corresponding to the target user's parents tend to be at home, so the causal score of the target user (target causal score) may be further based on the causal score of a co-living user who is the target user's parent (reference causal score). However, the reference user may be any user whose effect of the operation on the target user may be affected by the effect of the operation on the reference user, and is not limited to a co-living person.

Specifically, for example, the effect estimation unit 25 can estimate the reference causal score as the target causal score. That is, the effect estimation unit 25 may treat the causal score of a reference user identified as a reference user having a relationship with the target user as the causal score of the target user. In addition, at this time, when the reference causal score is a causal score that shows a higher effect than the target causal score, the effect estimation unit 25 may estimate the reference causal score as the target causal score.

The effect estimation unit 25 may estimate the target causal score based on data including multiple reference causal scores estimated for multiple reference users. For example, the effect estimation unit 25 may estimate the target causal score based on a statistical index (e.g., average, weighted average, median, mode, etc.) based on multiple reference causal scores estimated for multiple reference users. In addition, at this time, the effect estimation unit 25 may estimate the average (weighted average) of values obtained by weighting each of the multiple reference causal scores estimated for the multiple reference users according to the relationship strength (closeness score) indicating the closeness between the target user and the reference user as the target causal score. Note that the closeness score may be used as it is as the weight when calculating the weighted average, or a weight converted to another scale based on the closeness score may be used.

The risk estimation unit 26 estimates a risk based on the probability that the user will not perform the predetermined action described above. There is no limit to the method of expressing the risk, and various indices may be adopted. For example, the risk can be expressed using the probability that the receivable will be defaulted without being collected. In this embodiment, the risk estimation unit 26 estimates the risk based on the probability that the user will not perform the action using a machine learning model (hereinafter referred to as a "risk estimation model") that outputs a risk index indicating the probability that the receivable will be defaulted without being collected for the user in response to the input of one or more user attributes related to the target user. However, the risk may be estimated according to, for example, a predetermined rule without using a machine learning model. For example, the risk may be obtained by storing a value corresponding to each user attribute or combination of user attributes in advance and reading out this value. Indicators other than the probability of defaulting may be adopted as indicators of risk. For example, risks may be classified (ranked) and the class (rank) of the risk may be used as an indicator.

The machine learning unit 27 generates and/or updates an effect estimation model used for effect estimation by the effect estimation unit 25, and a risk estimation model used for risk estimation by the risk estimation unit 26. The effect estimation model is a machine learning model that outputs a causal score indicating the effectiveness of an operation on a user when one or more user attribute data related to a target user are input. The risk estimation model is a machine learning model that outputs a risk index indicating the degree of risk based on the likelihood that the user will not perform an action when one or more user attribute data related to a target user are input. The user attributes input to these machine learning models may include, for example, demographic attributes, behavioral attributes, or psychographic attributes. Here, demographic attributes include, for example, the user's gender, family structure, age, etc.; behavioral attributes include, for example, whether or not a cash advance has been used, whether or not a revolving payment has been used, deposit and withdrawal history for a specific account, and commercial transaction history for any product including gambling or lotteries (which may include online transaction history in an online marketplace, etc.); and psychographic attributes include, for example, preferences for gambling or lotteries. However, available user attributes are not limited to the examples given in this embodiment. For example, "time required for an operation (such as a phone call)" and "amount used by credit card" may also be used as attributes.

In generating and/or updating the effect estimation model, the machine learning unit 27 creates the effect estimation model based on teacher data (machine learning data) in which a score based on a statistic related to the execution rate of an action (debt collection rate) by a user who has received an operation among a plurality of users having a predetermined attribute and a statistic related to the execution rate of an action by a user who has not received an operation among a plurality of users is defined as a causal score indicating the effect of an operation on a user having the attribute. In this embodiment, for example, if the content of the operation is a call to a user, a causal score based on the difference between each statistic is calculated by the formula "(debt collection rate when the user makes a call) - (debt collection rate when the user does not make a call)", and the calculated causal score is combined with the attribute data of the corresponding user and input to the machine learning unit 27 as teacher data. In this embodiment, an example is described in which an average value is used as the statistic. However, as the statistic, a statistical indicator such as a mode or a median may be used. Here, the statistic related to the execution rate of the action may be based on the past debt collection rate of each user within a predetermined period (for example, a predetermined month). In addition, in this embodiment, a causal score is calculated based on the difference between the statistics, but if no statistically significant difference is found in the difference, the causal score may be set to zero or approximately zero. Here, existing statistical methods may be used to determine whether or not there is a significant difference. For example, the standard error and confidence interval for a set of the average response rate of users for each month can be taken into consideration, and the statistical significance of the change in response rate due to the presence or absence of a call can be taken into consideration. In this way, it is possible to calculate a causal score taking into account the variation in the average response rate of users in the same group.

When creating the teacher data, since a user who has been called once cannot become a user who has not been called thereafter, it is possible to obtain only one of the recovery rates when the user is called or when the user is not called. For this reason, teacher data on the effect of a call is created for each user group having a common attribute. That is, the causal score indicating the effect of a call on a user group consisting of users who have a certain common attribute is obtained by, for example, dividing a plurality of users having the common attribute into a first sub-user group that is called and a second sub-user group that is not called, calculating the average value of the debt recovery rate from the first sub-user group that is called and the average value of the debt recovery rate from the second sub-user group that is not called, respectively, and calculating the difference between these average recovery rates based on the above formula. For example, if the average value of the debt recovery rate from the first sub-user group that is called is 80% and the average value of the debt recovery rate from the second sub-user group that is not called is 70%, the causal score indicating the effect of a call on the user group is "10".

A machine learning model generation framework that can be adopted in implementing the technology disclosed herein is based on an ensemble learning algorithm, for example. For example, a machine learning framework (for example, LightGBM) based on a gradient boosting decision tree (GBDT) may be adopted as the framework. In other words, the framework may be a machine learning framework based on a decision tree model that transfers the error between the correct answer and the predicted value between the previous and next weak learners (weak classifiers). The predicted value here refers to, for example, the predicted value of the causal score or the risk index. In addition to LightGBM, the framework may adopt boosting methods such as XGBoost and CatBoost. According to a framework using a decision tree, a machine learning model with relatively high performance can be generated with less effort in parameter adjustment compared to a framework using a neural network. However, the framework for generating a machine learning model that can be used to implement the technology disclosed herein is not limited to the example in this embodiment. For example, instead of a gradient boosting decision tree, another learning device such as a random forest may be used as a learning device, or a learning device that is not called a weak learning device such as a neural network may be used. In particular, when a learning device that is not called a weak learning device such as a neural network is used, ensemble learning does not need to be used.

FIG. 13 is a simplified diagram showing the concept of a decision tree of the machine learning model employed in this embodiment. When adopting a gradient boosting machine learning framework based on a decision tree algorithm, the branching conditions of each node of the decision tree are optimized. Specifically, in the gradient boosting machine learning framework based on a decision tree algorithm, a causal score indicating the effect of an operation is calculated for each user group having attributes indicated by each of two child nodes branched from one parent node, and the branching condition of the parent node is optimized so that the difference between the causal scores becomes large (for example, so that the difference becomes maximum or is equal to or greater than a predetermined threshold), that is, so that the two child nodes branch neatly. For example, if the attribute indicated as the branching condition of the node is age, the age set as the branching threshold may be changed, or the branching condition may be changed to an attribute other than age. In this way, the branching conditions of all nodes of the decision tree are recursively optimized to improve the estimation accuracy of the effect of the operation.

When generating and/or updating the risk estimation model, the machine learning unit 27 creates a machine learning model based on training data in which a statistical amount (in this embodiment, an average value; however, a statistical index such as a mode or a median may be used) related to the default occurrence rate of multiple users having a predetermined attribute is defined as a risk index indicating the degree of risk of the user having the attribute for each user attribute. The calculated risk index is combined with the attribute data of the corresponding user and input to the machine learning unit 27 as training data. In addition, in generating or updating the risk estimation model, a machine learning model generation framework that can be adopted is not limited, but a machine learning framework of gradient boosting based on a decision tree algorithm may be adopted, as in the generation and/or update of the effect estimation model described above.

The condition output unit 28 determines and outputs conditions (hereinafter referred to as "operation conditions") related to operations to be performed on users at the operation center based on the estimated effects and risks (in this embodiment, the causal score and risk index). In this embodiment, the operation conditions are at least one of the following: whether the operation needs to be performed, the number of times the operation is performed, the order in which the operations are performed, the method of contacting the user during the operation, and the content of contacting the user during the operation. Here, examples of the contact method include making a call or sending a message, and examples of the content of contact include the content to be conveyed to the user during the call and the content of the message.

Figure 14 is a diagram showing the relationship between the estimated effects and risks and the operation conditions in this embodiment. Basically, the condition output unit 28 outputs operation conditions such that a higher priority is given to operations for users with higher estimated effects and at least some of the operations for users with higher estimated risks. Also, the condition output unit 28 outputs operation conditions such that a lower priority is given to operations for users with lower estimated effects and at least some of the operations for users with lower estimated risks.

Here, priority is a measure set for a user or an operation for that user, indicating the degree to which the operation is performed preferentially. The higher the priority of the user, the higher the possibility or frequency of receiving the operation, and the lower the priority of the user, the lower the possibility or frequency of receiving the operation. Specifically, the condition output unit 28 outputs an operation condition for setting the operation as required to be executed, increasing the number of times the operation is executed, speeding up the order of execution of the operation, or making the method or content of contacting the user in the operation more cost-effective or effective, as a condition for giving a higher priority. The condition output unit 28 also outputs an operation condition for setting the operation as not required to be executed, decreasing the number of times the operation is executed, delaying the order of execution of the operation, or making the method or content of contacting the user in the operation more cost-effective or effective, as a condition for giving a lower priority.

In this embodiment, the condition output unit 28 compares the estimated causal score and risk index with the thresholds previously set for the causal score and the risk index, respectively, and determines and outputs the operation conditions according to the comparison results. Specifically, in the example shown in FIG. 14, a threshold C1 and a threshold C2 larger than the threshold C1 are set for the causal score, and a threshold R1 and a threshold R2 larger than the threshold R1 are set for the risk index. In addition, for the risk index, a threshold R3 for determining whether or not to set an operation condition according to the causal score, and a threshold R4 for determining a user for whom a high-priority operation condition is set regardless of the causal score are further set. Note that in this embodiment, an example will be described in which the same value is adopted for the threshold R1 and the threshold R3, and the same value is adopted for the threshold R2 and the threshold R4 (see FIG. 14), but different values may be adopted for these thresholds.

Here, a case where an operation condition with a high priority is output will be described.
Case (1): The condition output unit 28 determines and outputs high-priority operation conditions for at least some of the users whose causal scores are equal to or greater than the threshold C2 because the operation is highly effective.
Case (2): Furthermore, for at least some of the users whose risk index is equal to or greater than the threshold value R2, the condition output unit 28 determines and outputs high-priority operation conditions because the risk is high.
Case (3): In particular, the condition output unit 28 may determine and output the highest priority operation condition for a user whose causal score is equal to or greater than the threshold C2 and whose risk index is equal to or greater than the threshold R2 (see the area UR indicated by the dashed line in Figure 14).
Case (4): However, for users whose estimated risk index is lower than the threshold R3, the condition output unit 28 may not output operation conditions that are given high priority because the possibility of defaulting is low in the first place. When it is desired to enhance the cost suppression effect, it is preferable to determine and output low-priority operation conditions or medium-priority operation conditions regardless of the causal score for users whose risk index is lower than the threshold R3 (the area UL shown by the dashed line in FIG. 14 has a causal score of C2 or more, but the risk index is less than R3, so it is not a high-priority operation condition). For this reason, the effect estimation unit 25 may estimate the effect of the operation for users whose risk index is estimated to be equal to or higher than the threshold R3, and may not estimate the effect of the operation for users whose risk index is estimated to be less than the threshold R3 (omit the estimation process).

A case where an operation condition with a low priority is output will also be described.
Case (5): For at least some of the users whose causal scores are less than the threshold C1, the condition output unit 28 determines and outputs low-priority operation conditions because the operation is less effective.
Case (6): Furthermore, for at least some of the users whose risk index is less than the threshold value R1, the condition output unit 28 determines and outputs low-priority operation conditions because the risk is low.
Case (7): In particular, the condition output unit 28 may determine and output the lowest priority operation condition for a user whose causal score is less than the threshold C1 and whose risk index is less than the threshold R1 (see area LL indicated by the dashed line in Figure 14).
Case (8): However, for a user whose estimated risk index is equal to or greater than the threshold R4, the condition output unit 28 may not output an operation condition that is given a low priority because the operation condition is likely to be the default in the first place. Rather, for a user whose risk index is equal to or greater than the threshold R4, it is preferable to determine and output an operation condition with a high priority regardless of the causal score (the region LR indicated by the dashed line in FIG. 14 has a causal score less than C1, but the risk index is equal to or greater than R4, so the operation condition does not have a low priority).

As a result, in the example shown in FIG. 14, the operation priority is increased for high-risk users and medium-risk and high-effect users, and decreased for low-risk users and medium-risk and low-effect users. Note that in the above example, the area is divided based on a threshold value, and a common operation condition is determined for users belonging to one area, but different operation conditions may be set for each user or for each user in one area. For example, even within the same area, the operation conditions may be different to provide a gradation according to the high and low causal scores and/or risk indexes.

The amount of operations by the operation center management system 3 (the total number of operations and the number of users subject to the operations) can be changed by adjusting the thresholds described above. For example, it is possible to increase the amount of operations by lowering at least one of thresholds C1, C2, R1, and R2, and it is possible to decrease the amount of operations by raising at least one of thresholds C1, C2, R1, and R2.

<Processing flow>
Next, a process flow executed by the information processing system according to the present embodiment will be described. Note that the specific contents and processing order of the processes described below are an example for implementing the present disclosure. The specific contents and processing order may be appropriately selected according to the embodiment of the present disclosure.

Figure 15 is a flowchart showing the flow of the machine learning process according to this embodiment. The process shown in this flowchart is executed at a timing specified by the administrator of the operation center management system 3.

In steps S101 and S102, an effect estimation model is generated and/or updated. The machine learning unit 27 calculates a causal score for each of a plurality of user attributes based on the user attribute data, operation history data, and credit card usage payment history data previously accumulated in the operation center management system 3 or the credit card management system 5, and creates teacher data including a combination of the user attributes and the causal score (step S101). Here, the operation history data includes data that can be used to determine whether or not an operation has been performed for each user, and the payment history data includes data that can be used to determine whether or not the user has paid the credit card usage amount (whether or not there is a default). Then, the machine learning unit 27 inputs the created teacher data into the machine learning model, and generates or updates a machine learning model (effect estimation model) used for effect estimation by the effect estimation unit 25 (step S102). After that, the process proceeds to step S103.

In steps S103 and S104, a risk estimation model is generated and/or updated. The machine learning unit 27 calculates a risk index for each of multiple user attributes based on user attribute data, operation history data, and credit card usage payment history data previously accumulated in the operation center management system 3 or the credit card management system 5, and creates training data including a combination of user attributes and risk indexes (step S103). Then, the machine learning unit 27 inputs the created training data into the machine learning model, and generates or updates a machine learning model (risk estimation model) used for risk estimation by the risk estimation unit 26 (step S104). Thereafter, the processing shown in this flowchart ends.

Figure 16 is a flowchart showing the flow of the operation condition output process according to this embodiment. The process shown in this flowchart is executed at a preset timing each month. More specifically, the execution timing of the process is set to be after the specified date for payment of the credit card usage amount and before the scheduled date for execution of the operation for the unpaid user. Note that, in this case, graph data has already been generated for multiple users including the target user, and each machine learning model has already been trained.

In step S201, a risk is estimated based on the probability that the user will not perform an action. For each of a plurality of users, the risk estimation unit 26 inputs one or more pieces of user attribute data related to the target user into the risk estimation model generated and/or updated in step S104, and obtains, as an output, a risk index corresponding to the target user. Then, the process proceeds to step S202.

In step S202, it is determined whether or not a causal score can be obtained based on the attribute data. The information processing device 1 determines whether or not user attribute data can be obtained from the system for the target user to an extent that a causal score corresponding to the target user can be obtained from the effect estimation model. This determination may be made simply based on the number of attribute data, or based on the presence or absence of one or more types of predetermined attribute data that are important for obtaining a causal score. In addition, the one or more types of predetermined attribute data that are important for obtaining a causal score may be determined based on the accuracy of past effect estimation results using the effect estimation model. If it is determined that sufficient attribute data for estimating a causal score can be obtained from the system, the process proceeds to step S203.

In step S203, the effect of the operation on the target user is estimated. For each of the multiple users, the effect estimation unit 25 inputs one or more user attribute data related to the target user into the effect estimation model generated and/or updated in step S102, and obtains, as output, a causal score corresponding to the target user. Then, the process proceeds to step S207.

On the other hand, if it is determined in step S202 that sufficient attribute data cannot be obtained from the system to estimate the causal score, processing proceeds to step S204.

In steps S204 and S205, the reference user is identified, and the effect of the operation on the reference user is estimated. The reference user identification unit 22 refers to the graph data and identifies one or more other users corresponding to the node data 50 that is connected to the node data 50 corresponding to the target user by an explicit link or an implicit link as the reference user. Here, for example, a user who has a parent-child relationship, a marital relationship, or a friendship relationship with the target user and lives in the same household is identified as the reference user (step S204). When the reference user is identified, the effect estimation unit 25 inputs one or more user attribute data related to the reference user to the effect estimation model generated and/or updated in step S102 for the reference user, and obtains a causal score (reference causal score) corresponding to the reference user as an output (step S205). After that, the process proceeds to step S206.

In step S206, the effect of the operation on the target user is estimated based on the effect of the operation on the reference user. The effect estimation unit 25 acquires a causal score corresponding to the target user based on data including the reference causal score acquired in step S205 for the target user. As described above, the effect estimation unit 25 may estimate the reference causal score as the target causal score, or may estimate the target causal score based on data including multiple reference causal scores estimated for multiple reference users. Then, the process proceeds to step S207.

In step S207, operation conditions are determined and output. The condition output unit 28 determines operation conditions based on the causal score of the target user acquired in step S203 or step S206 and the risk index of the target user acquired in step S201, and outputs the operation conditions to the operation center management system 3. In this embodiment, the condition output unit 28 identifies and outputs operation conditions that are pre-mapped to the causal score and risk index. However, the method of determining the operation conditions is not limited to the example in this embodiment. For example, the operation conditions may include values calculated by inputting the causal score and risk index into a specified function. Thereafter, the processing shown in this flowchart ends.

When the operation conditions are output, the operation center management system 3 manages the operation for the target user according to the operation conditions, and the operation terminal executes the operation according to the instructions output by the operation center management system 3.

＜Effects＞
According to this embodiment, by setting priorities of operation conditions according to the effect and risk of operations for each user and suppressing operations for users with low effect or risk, it is possible to suppress the cost related to operations without lowering the debt collection rate. In other words, according to the present disclosure, it is possible to suppress the cost for operations without reducing the effect of operations that prompt users to take a predetermined action. Furthermore, according to this embodiment, it is expected that the debt collection rate can be increased while suppressing costs by increasing operations for users with high effect or risk.

<Variation of operation condition output process (1)>
In the embodiment described above, the flow of the operation condition output process has been generally described with reference to FIG. 16. More specifically, the operation condition output process may be processed as follows.

Figure 17 is a flowchart showing the flow of the operation condition output process in this embodiment when the judgment methods of cases (1) to (4) described with reference to Figure 14 are adopted. According to the example shown in Figure 17, when the risk index calculated by the risk estimation unit 26 (step S301) for a user having a certain attribute is less than the threshold value R3 (NO in step S302), the calculation of the causal score by the effect estimation unit 25 is omitted, and an operation condition with low (or medium) priority is determined and output (step S303).

If the risk index calculated for the user is equal to or greater than threshold R3 (YES in step S302), a causal score is calculated (step S304). If the causal score is equal to or greater than threshold C2 and the risk index is equal to or greater than threshold R2 (YES in step S305), the highest priority operation condition is determined and output (step S306). If the causal score is equal to or greater than threshold C2 or the risk index is equal to or greater than threshold R2 (YES in step S307), a high priority operation condition is determined and output (step S308). If the causal score is less than threshold C2 and less than threshold R2, a medium priority operation condition is determined and output (step S309).

<Variation of Operation Condition Output Processing (2)>
Fig. 18 is a flowchart showing the flow of the operation condition output process in the case where the judgment method of cases (5) to (8) described with reference to Fig. 14 is adopted in this embodiment. According to the example shown in Fig. 18, when the risk index calculated by the risk estimation unit 26 (step S401) for a user having a certain attribute is equal to or greater than the threshold value R4 (NO in step S402), the calculation of the causal score by the effect estimation unit 25 is omitted, and an operation condition with high (or medium) priority is determined and output (step S403).

If the risk index calculated for the user is less than threshold R4 (YES in step S402), a causal score is calculated (step S404). If the causal score is less than threshold C1 and the risk index is less than threshold R1 (YES in step S405), an operation condition with the lowest priority is determined and output (step S406). If the causal score is less than threshold C1 or the risk index is less than threshold R1 (YES in step S407), an operation condition with a low priority is determined and output (step S408). Note that if the causal score is equal to or greater than threshold C1 and equal to or greater than threshold R1 (NO in step S407), an operation condition with a medium priority is determined and output (step S409).

<Variation of Operation Condition Output Processing (3)>
In the above-described embodiment, an example was described in which two axes, a causal score indicating the effect of an operation and a risk index indicating a risk, are used as evaluation axes for determining operation conditions, but in the technology disclosed herein, the evaluation axes for determining operation conditions only need to include at least the effect of the operation, and other evaluation axes may be adopted, or three or more evaluation axes may be adopted. For example, (1) a third index other than the risk index may be adopted as an index to be combined with the causal score, (2) a third index may be adopted in addition to the causal score and the risk index, or (3) three axes, a causal score for a call, a causal score for a message transmission, and a risk index, may be adopted.

For example, the operation conditions may be set with reference to the target causal score and the reference causal score (without reference to the risk index). In this case, the condition output unit 28 outputs an operation condition such that a higher priority is given to operations for users with a higher target causal score and at least some of the operations for users with a higher reference causal score. The condition output unit 28 also outputs an operation condition such that a lower priority is given to operations for users with a lower target causal score and at least some of the operations for users with a lower reference causal score.

<Variation (4) of Operation Condition Output Processing>
As described above, in the technology according to the present disclosure, three or more evaluation axes may be adopted as the evaluation axes for determining the operation conditions. In this variation, the condition output unit 28 is different from the condition output unit 28 according to the embodiment described above in that, as the effect (causal score), in addition to the effect (target causal score) and risk index estimated for the target user, the condition output unit 28 also refers to the effect (reference causal score) estimated for the reference user, and outputs the condition regarding the operation for the target user. That is, the condition output unit 28 according to this variation outputs the operation condition for the target user based on data including the target causal score and the reference causal score.

In this variation, as in the embodiment described above, the condition output unit 28 outputs operation conditions such that, in principle, a higher priority is given to operations for users with a higher estimated effect and at least some of the operations for users with a higher estimated risk, and outputs operation conditions such that a lower priority is given to operations for users with a lower estimated effect and at least some of the operations for users with a lower estimated risk.

In this variation, the condition output unit 28 basically outputs an operation condition such that a higher priority is given to at least some of the operations for users with a higher target causal score, the operations for users with a higher reference causal score, and the operations for users with a higher estimated risk. The condition output unit 28 also outputs an operation condition such that a lower priority is given to at least some of the operations for users with a lower target causal score, the operations for users with a lower reference causal score, and the operations for users with a lower estimated risk.

In addition, in this variation, the condition output unit 28 outputs a condition under which at least some of the operations on a target user for which the estimated effect (reference causal score) for the reference user is higher are given a higher priority than the priority that would be determined if the reference causal score were not referenced. In this case, the condition output unit 28 may compare the reference causal score with the target causal score to determine whether the reference causal score is higher, and if the reference causal score is higher than the target causal score, output a condition under which at least some of the operations on the target user are given a higher priority than the priority that would be determined if the reference causal score were not referenced.

Furthermore, in this variation, instead of or in addition to the above-mentioned method of setting the operation conditions, the condition output unit 28 may output a condition such that at least some of the operations for a target user having a lower effect (reference causal score) estimated for the reference user are given a lower priority than the priority that would be determined if the reference causal score were not referenced. In this case, the condition output unit 28 may compare the reference causal score with the target causal score to determine whether the reference causal score is lower, and if the reference causal score is lower than the target causal score, output a condition such that at least some of the operations for the target user are given a lower priority than the priority that would be determined if the reference causal score were not referenced.

<Variation of Operation Condition Output Process (5)>
The setting of the operation condition that refers to the reference causal score in addition to the target causal score, as described in the variation (4) of the operation condition output process, may be implemented, more specifically, in the manner described below. However, this variation does not limit the specific embodiment of the variation (4).

Figure 19 is a diagram showing the relationship between the target causal score, reference causal score, and risk index estimated in this variation and the operation conditions. In this variation, the condition output unit 28 compares the estimated target causal score, reference causal score, and risk index with preset thresholds for the target causal score, reference causal score, and risk index, respectively, and determines and outputs the operation conditions according to the comparison results. In the example shown in Figure 19, as in the embodiment described above, thresholds C1, C2, R1, R2, R3, and R4 are set, and the roles of these thresholds are the same as in the embodiment described above.

As a difference from the embodiment described above, in this variation, when the target causal score is within a first range that is neither the high effect range (threshold C2 or more) nor the low effect range (below threshold C1) (i.e., within the medium effect range that is equal to or more than threshold C1 and less than C2), and the estimated risk index is within a second range that is neither the high risk range (threshold R2 or more) nor the low risk range (below threshold R1) (i.e., within the medium risk range that is equal to or more than threshold R1 and less than R2) (see range A1 surrounded by a dashed rectangle in the figure), the condition output unit 28 outputs a condition that gives a higher priority to at least some of the operations for the target user whose reference causal score is higher (e.g., equal to or more than a preset threshold RC2). In other words, in this variation, even if the target causal score of the target user is estimated to be in the medium effect range, if the causal score of the reference user is higher and the risk index of the target user is estimated to be medium or higher, the condition output unit 28 outputs an operation condition with a higher priority for at least some of the operations for the target user.

In addition, in this variation, when the target causal score is within a third range (less than threshold C1) for determining that the target causal score is in the low-effect zone, and the estimated risk index is within a second range that is neither the high-risk zone (above threshold R2) nor the low-risk zone (below threshold R1) (i.e., within the medium-risk zone between thresholds R1 and R2) (see range A2 enclosed by a dashed rectangle in the figure), the condition output unit 28 outputs a condition under which a higher priority is given to at least some of the operations for a user with a higher reference causal score (e.g., a preset threshold RC2 or higher). In other words, in this variation, even if the target causal score of the target user is estimated to be in the low-effect zone, if the causal score of the reference user is higher and the risk index of the target user is estimated to be at least medium, the condition output unit 28 outputs an operation condition with a higher priority for at least some of the operations for the target user.

<Variation of Operation Condition Output Process (6)>
Instead of or in addition to the method of setting the operation conditions described in the variation (5) of the operation condition output process, the operation conditions described below may be set. However, this variation does not limit the specific embodiment of the variation (4).

Figure 20 is a diagram showing the relationship between the target causal score, reference causal score, and risk index estimated in this variation and the operation conditions. In this variation, the condition output unit 28 compares the estimated target causal score, reference causal score, and risk index with preset thresholds for the target causal score, reference causal score, and risk index, respectively, and determines and outputs the operation conditions according to the comparison results. In the example shown in Figure 20, as in the embodiment described above, thresholds C1, C2, R1, R2, R3, and R4 are set, and the roles of these thresholds are the same as in the embodiment described above.

In this variation, when the target causal score is within a first range that is neither the high effect range (threshold C2 or more) nor the low effect range (below threshold C1) (i.e., within the medium effect range that is equal to or more than threshold C1 and less than C2), and the estimated risk index is within a second range that is neither the high risk range (threshold R2 or more) nor the low risk range (below threshold R1) (i.e., within the medium risk range that is equal to or more than threshold R1 and less than R2) (see the range A1 surrounded by a dashed rectangle in the figure), the condition output unit 28 outputs a condition such that at least some of the operations for a user with a lower reference causal score (for example, less than a preset threshold RC1) are given a lower (or medium) priority. In other words, in this variation, even if the target causal score of the target user is estimated to be in the medium effect range, if the causal score of the reference user is lower and the risk index of the target user is estimated to be medium or higher, the condition output unit 28 outputs an operation condition with a lower (or medium) priority for at least some of the operations for the target user.

In addition, in this variation, when the target causal score is within a third range (less than threshold C1) for determining that the target causal score is in the low-effect zone, and the estimated risk index is within a second range that is neither the high-risk zone (above threshold R2) nor the low-risk zone (below threshold R1) (i.e., within the medium-risk zone between thresholds R1 and R2) (see range A2 surrounded by a dashed rectangle in the figure), the condition output unit 28 outputs a condition for giving a lower (or medium) priority to at least some of the operations for the target user whose reference causal score is lower (for example, less than a preset threshold RC1). In other words, in this variation, when the target causal score of the target user is estimated to be in the low-effect zone, the causal score of the reference user is lower, and the risk index of the target user is estimated to be at least medium, the condition output unit 28 outputs an operation condition with a lower (or medium) priority for at least some of the operations for the target user.

<Variation of Operation Condition Output Processing (7)>
21 and 22 are flowcharts showing the flow of the operation condition output process when the relationship between the target causal score, reference causal score, and risk index described with reference to FIGS. 19 and 20 and the operation conditions is adopted in this embodiment.

According to the example shown in this flowchart, if the risk index calculated by the risk estimation unit 26 (step S501) for a user with a certain attribute is less than the threshold R3 (NO in step S501), the calculation of the causal score by the effect estimation unit 25 is omitted, and an operation condition with a low priority is determined and output (step S503). Also, if the risk index calculated for the user is equal to or greater than the threshold R4 (NO in step S504), the calculation of the causal score by the effect estimation unit 25 is omitted, and an operation condition with a high priority is determined and output (step S505). Once the operation condition has been determined and output, the processing shown in this flowchart ends.

If the calculated risk index for the user is equal to or greater than threshold R3 and less than threshold R4 (i.e., medium risk), a target causal score and a reference causal score are calculated (step S506). If the calculated reference causal score is determined to be in the low-effect range (e.g., less than threshold RC1) (YES in step S507), a low-priority operation condition is determined and output (step S508). If the calculated reference causal score is determined to be in the high-effect range (e.g., equal to or greater than threshold RC2) (YES in step S509), a high-priority operation condition is determined and output (step S510).

If it is determined that the reference causal score calculated for the user is neither in the low effect zone nor the high effect zone (in other words, in the medium effect zone), an operation condition according to the target causal score is determined and output in a manner similar to that described with reference to FIG. 14 (step S511). That is, if the target causal score is less than the threshold C1, an operation condition with low priority is determined and output, if the target causal score is equal to or greater than the threshold C1 and less than the threshold C2, an operation condition with medium priority is determined and output, and if the target causal score is equal to or greater than the threshold C2, an operation condition with high priority is determined and output.

In the above example, an example was described in which the area is divided based on a threshold value and the operation conditions are determined. However, as described above in the explanation of FIG. 14, different operation conditions may be set for each user or for each user even within the same area. For example, even within the same area, the operation conditions may be different so as to have a gradation according to the level of the causal score and/or risk index.

<Other variations>
In the above-described embodiment, an example was described in which the operation for the user is a call, but the type of operation for the user is not limited to a call. For example, a message transmission may be adopted as the type of operation for the user. Note that the means for transmitting the message is not limited here, and an email system, a short message service (SMS), or a message transmission and reception service of a social networking service (SNS) may be used.

In the above-described embodiment, an example has been described in which the operation condition is determined based on the effect estimated for one type of operation (call), but the operation condition may be determined based on the effect estimated for each of multiple types of operations (e.g., calling and sending a message). In this case, the effect estimation unit 25 estimates a first effect that a first operation (e.g., calling) for a user to prompt the user to perform a predetermined action has on whether the user will perform the action, and a second effect that a second operation (e.g., sending a message) for a user to prompt the user to perform a predetermined action has on whether the user will perform the action, and the condition output unit 28 outputs the operation condition for the user based on the estimated first effect and second effect.

In this case, a machine learning model for estimating the effect of an operation is also generated and updated for each type of operation. For example, if the first operation is making a call and the second operation is sending a message, a machine learning model for estimating the effect of making a call and a machine learning model for estimating the effect of sending a message may be generated and updated.

Furthermore, when the operation conditions are determined based on the effects estimated for each of the multiple types of operations, a type of operation that is highly effective for the user may be selected from the multiple types of operations. In this case, the condition output unit 28 outputs an operation condition including whether to perform the first operation or the second operation on the user based on the estimated first effect and second effect. More specifically, the first effect (causal score related to the first operation) and the second effect (causal score related to the second operation) obtained for the target user can be compared, and the type of operation with the higher causal score can be selected as the type of operation that is highly effective for the user.

In the above-described embodiment, the difference between the action execution rate of the sub-user group that received the operation and the action execution rate of the sub-user group that did not receive the operation is used as the causal score, but the causal score may be calculated by other methods. For example, when generating and/or updating the effect estimation model, the machine learning unit 27 may create a machine learning model based on teacher data in which, for each user attribute, a score based on a statistic related to the action execution rate of a user who has a predetermined attribute and has performed a predetermined reaction to the operation, and a statistic related to the action execution rate of a user who has not performed a predetermined reaction, is defined as a causal score related to the user having the attribute. For example, in this variation, the causal score is calculated by the formula "(the debt collection rate when the user performs a predetermined reaction) - (the debt collection rate when the user does not perform a predetermined reaction)". In this case, the condition output unit 28 may output a condition related to the operation based on the presence or absence of a reaction by the user, the content of the reaction, etc., and the causal score according to the reaction. At this time, the priority of the operation condition output by the condition output unit 28 may be adjusted using the relationship between the causal score and the priority described with reference to FIG. 14.

Here, the specified reaction is, for example, a user's response to a call by dialing, a conversation with an operator when the user calls back on a call, a reply to a message, marking a message as read, etc. Furthermore, the content of the reaction may be considered to be a positive response to a payment, or the presence or absence of a response to the due date of payment, etc. If the user's reaction is a voice reaction, it is also possible to determine the user's emotions, etc. based on the user's voice and determine whether the reaction was positive or not. Furthermore, the priority of the next operation condition may be adjusted based on the user's emotions, etc. determined based on the voice.

The priority of the operation conditions may also be adjusted based on factors other than those mentioned above. For example, for a user whose credit card payment settings are revolving payments or installments, the priority of the operation conditions may be higher than that of a user whose credit card payment settings are lump sum payments, and for a user whose credit card usage includes cash advances, the priority of the operation conditions may be higher than that of a user whose credit card usage does not include cash advances. The priority of the operation conditions may also be adjusted according to transaction data other than that of the target user's credit card (for example, balance data of the credit card usage amount withdrawal account, transaction history data at affiliated banks, etc.). Here, the condition output unit 28 may adjust various thresholds corresponding to the various scores shown in FIG. 14, for example, based on the credit card usage conditions.

In addition, in the embodiment described above, an example of an information processing device including a graph data generation unit 21, a reference user identification unit 22, a relationship identification unit 23, a relationship strength determination unit 24, an effect estimation unit 25, a risk estimation unit 26, a machine learning unit 27, and a condition output unit 28 was described, but some of these functional units may be omitted to the extent that the invention disclosed herein can be implemented.

For example, in the embodiment described above, the relationship strength (closeness score) between users was generated and referenced when identifying the reference user or calculating the target causal score, but the generation and reference of the closeness score may be omitted when identifying the reference user or calculating the target causal score. In this case, of the functional units of the information processing device 1 described with reference to FIG. 2, the relationship strength determination unit 24 may be omitted.

1. Information processing device

Claims (10)

an effect estimation means for estimating an effect of a predetermined operation for a user for encouraging the user to perform a predetermined action on whether or not the user will perform the action, based on a causal score based on a difference between a statistical amount relating to a rate of execution of the action by users among a plurality of users who have received the operation and a statistical amount relating to a rate of execution of the action by users among the plurality of users who have not received the operation;
a risk estimation means for estimating a risk based on the probability that the user will not perform the action by using a model that outputs a risk index indicating the degree of risk based on the probability that the user will not perform the action in response to an input of one or more attributes related to the user;
A relationship specifying means for specifying a relationship between users based on a result of clustering based on values associated with the relationship between users;
a relationship strength determination means for determining a relationship strength indicating a closeness between the users based on an index indicating the strength of the relationship between the users in accordance with a judgment criterion corresponding to the relationship between the users;
A reference user specifying means for specifying a reference user having a relationship with the target user according to the relationship strength ;
and a condition output means for outputting a condition regarding the operation for the target user based on data including a target effect, which is the effect estimated for the target user, a reference effect , which is the effect estimated for the reference user, and the risk, the condition output means outputting at least one of the following conditions (a) to (j):
(a) a condition under which higher priority is given to at least some of the operations for users with higher target effectiveness, the operations for users with higher reference effectiveness, and the operations for users with higher estimated risk;
(b) a condition under which a lower priority is given to at least some of the operations for users with lower target effects, the operations for users with lower reference effects, and the operations for users with lower estimated risks;
(c) a condition under which at least some of the operations for users with a higher reference effect are given a higher priority than would be determined without reference to the reference effect;
(d) a condition under which at least some of the operations for a user for which the reference effect is higher than the target effect are given a higher priority than a priority determined without reference to the reference effect;
(e) a condition in which, when the target effect is within a first range that is neither a high effect zone nor a low effect zone, and the estimated risk is within a second range that is neither a high risk zone nor a low risk zone, a higher priority is given to at least a portion of the operations for users with a higher reference effect;
(f) a condition in which, when the target effect is within a third range for determining that the target effect is a low effect zone, and the estimated risk is within a second range that is neither a high risk zone nor a low risk zone, a higher priority is given to at least a portion of the operations for users with a higher reference effect;
(g) a condition under which at least some of the operations for a user with a lower reference effect are given a lower priority than would be determined if the reference effect were not referenced;
(h) a condition under which at least some of the operations for a user for whom the reference effect is lower than the target effect are given a lower priority than a priority determined without reference to the reference effect;
(i) a condition in which, if the target effect is within a first range that is neither a high effect zone nor a low effect zone, and the estimated risk is within a second range that is neither a high risk zone nor a low risk zone, a lower priority is given to at least some of the operations for users with a lower reference effect;
(j) a condition in which, when the target effect is within a third range for determining that the target effect is a low effect zone, and the estimated risk is within a second range that is neither a high risk zone nor a low risk zone, a lower priority is given to at least a portion of the operations for users with a lower reference effect;
Information processing device. an effect estimation means for estimating an effect of a predetermined operation for a user for encouraging the user to perform a predetermined action on whether or not the user will perform the action, based on a causal score based on a difference between a statistical amount relating to a rate of execution of the action by users among a plurality of users who have received the operation and a statistical amount relating to a rate of execution of the action by users among the plurality of users who have not received the operation;
a risk estimation means for estimating a risk based on the probability that the user will not perform the action by using a model that outputs a risk index indicating the degree of risk based on the probability that the user will not perform the action in response to an input of one or more attributes related to the user;
A reference user specification means for specifying a reference user having a relationship with the target user based on graph data indicating the relationship between the users ;
and a condition output means for outputting a condition regarding the operation for the target user based on data including a target effect, which is the effect estimated for the target user, a reference effect , which is the effect estimated for the reference user, and the risk, the condition output means outputting at least one of the following conditions (a) to (j):
(a) a condition under which higher priority is given to at least some of the operations for users with higher target effectiveness, the operations for users with higher reference effectiveness, and the operations for users with higher estimated risk;
(b) a condition under which a lower priority is given to at least some of the operations for users with lower target effects, the operations for users with lower reference effects, and the operations for users with lower estimated risks;
(c) a condition under which at least some of the operations for users with a higher reference effect are given a higher priority than would be determined without reference to the reference effect;
(d) a condition under which at least some of the operations for a user for which the reference effect is higher than the target effect are given a higher priority than a priority determined without reference to the reference effect;
(e) a condition in which, when the target effect is within a first range that is neither a high effect zone nor a low effect zone, and the estimated risk is within a second range that is neither a high risk zone nor a low risk zone, a higher priority is given to at least a portion of the operations for users with a higher reference effect;
(f) a condition in which, when the target effect is within a third range for determining that the target effect is a low effect zone, and the estimated risk is within a second range that is neither a high risk zone nor a low risk zone, a higher priority is given to at least a portion of the operations for users with a higher reference effect;
(g) a condition under which at least some of the operations for a user with a lower reference effect are given a lower priority than would be determined if the reference effect were not referenced;
(h) a condition under which at least some of the operations for a user for whom the reference effect is lower than the target effect are given a lower priority than a priority determined without reference to the reference effect;
(i) a condition in which, if the target effect is within a first range that is neither a high effect zone nor a low effect zone, and the estimated risk is within a second range that is neither a high risk zone nor a low risk zone, a lower priority is given to at least some of the operations for users with a lower reference effect;
(j) a condition in which, when the target effect is within a third range for determining that the target effect is a low effect zone, and the estimated risk is within a second range that is neither a high risk zone nor a low risk zone, a lower priority is given to at least a portion of the operations for users with a lower reference effect;
Information processing device. the condition output means outputs a condition under which a higher priority is given to the operation for a user having a higher target effect and at least a part of the operation for a user having a higher reference effect.
The information processing device according to claim 1 . the condition output means outputs a condition under which a lower priority is given to the operation for the user having a lower target effect and at least a part of the operation for the user having a lower reference effect.
The information processing device according to claim 1 . the effect estimation means estimates an effect of the operation using an effect estimation model that outputs a causal score indicating an effect of the operation on the user in response to an input of one or more attributes related to the user;
the effect estimation model is created based on training data in which a score based on a statistic relating to an execution rate of the action by a user who has received the operation among a plurality of users having a predetermined attribute and a statistic relating to an execution rate of the action by a user who has not received the operation among the plurality of users is defined as a causal score indicating an effect of the operation on a user who has the attribute;
The information processing device according to claim 1 . The condition regarding the operation is at least one of the following: whether or not the operation needs to be performed, the number of times the operation needs to be performed, the order in which the operation needs to be performed, a method of contacting a user in the operation, and the content of contacting a user in the operation.
The information processing device according to claim 1 . The computer
an effect estimation step of estimating an effect of a predetermined operation on a user for encouraging the user to perform a predetermined action on whether or not the user will perform the action by using a causal score based on a difference between a statistical amount relating to a rate of execution of the action by users among a plurality of users who have received the operation and a statistical amount relating to a rate of execution of the action by users among the plurality of users who have not received the operation;
a risk estimation step of estimating a risk based on the probability that the user will not perform the action by using a model that outputs a risk index indicating the degree of risk based on the probability that the user will not perform the action in response to an input of one or more attributes related to the user;
A relationship specifying step of specifying a relationship between users based on a result of clustering based on values associated with relationships between users;
a relationship strength determination step of determining a relationship strength indicating a closeness between the users based on an index indicating the strength of the relationship between the users in accordance with a judgment criterion corresponding to the relationship between the users;
a reference user identifying step of identifying a reference user having a relationship with the target user according to the relationship strength ;
a condition output step of outputting a condition regarding the operation for the target user based on data including a target effect , which is the effect estimated for the target user, a reference effect, which is the effect estimated for the reference user, and the risk, the condition output step outputting at least one of the following conditions (a) to (j):
(a) a condition under which higher priority is given to at least some of the operations for users with higher target effectiveness, the operations for users with higher reference effectiveness, and the operations for users with higher estimated risk;
(b) a condition under which a lower priority is given to at least some of the operations for users with lower target effects, the operations for users with lower reference effects, and the operations for users with lower estimated risks;
(c) a condition under which at least some of the operations for users with a higher reference effect are given a higher priority than would be determined without reference to the reference effect;
(d) a condition under which at least some of the operations for a user for which the reference effect is higher than the target effect are given a higher priority than a priority determined without reference to the reference effect;
(e) a condition in which, when the target effect is within a first range that is neither a high effect zone nor a low effect zone, and the estimated risk is within a second range that is neither a high risk zone nor a low risk zone, a higher priority is given to at least a portion of the operations for users with a higher reference effect;
(f) a condition in which, when the target effect is within a third range for determining that the target effect is a low effect zone, and the estimated risk is within a second range that is neither a high risk zone nor a low risk zone, a higher priority is given to at least a portion of the operations for users with a higher reference effect;
(g) a condition under which at least some of the operations for a user with a lower reference effect are given a lower priority than would be determined if the reference effect were not referenced;
(h) a condition under which at least some of the operations for a user for whom the reference effect is lower than the target effect are given a lower priority than a priority determined without reference to the reference effect;
(i) a condition in which, if the target effect is within a first range that is neither a high effect zone nor a low effect zone, and the estimated risk is within a second range that is neither a high risk zone nor a low risk zone, a lower priority is given to at least some of the operations for users with a lower reference effect;
(j) a condition in which, when the target effect is within a third range for determining that the target effect is a low effect zone, and the estimated risk is within a second range that is neither a high risk zone nor a low risk zone, a lower priority is given to at least a portion of the operations for users with a lower reference effect;
method . The computer
an effect estimation step of estimating an effect of a predetermined operation on a user for encouraging the user to perform a predetermined action on whether or not the user will perform the action by using a causal score based on a difference between a statistical amount relating to a rate of execution of the action by users among a plurality of users who have received the operation and a statistical amount relating to a rate of execution of the action by users among the plurality of users who have not received the operation;
a risk estimation step of estimating a risk based on the probability that the user will not perform the action by using a model that outputs a risk index indicating the degree of risk based on the probability that the user will not perform the action in response to an input of one or more attributes related to the user;
A reference user identifying step of identifying a reference user having a relationship with the target user based on graph data indicating the relationship between the users ;
a condition output step of outputting a condition regarding the operation for the target user based on data including a target effect , which is the effect estimated for the target user, a reference effect, which is the effect estimated for the reference user, and the risk, the condition output step outputting at least one of the following conditions (a) to (j):
(a) a condition under which higher priority is given to at least some of the operations for users with higher target effectiveness, the operations for users with higher reference effectiveness, and the operations for users with higher estimated risk;
(b) a condition under which a lower priority is given to at least some of the operations for users with lower target effects, the operations for users with lower reference effects, and the operations for users with lower estimated risks;
(c) a condition under which at least some of the operations for users with a higher reference effect are given a higher priority than would be determined without reference to the reference effect;
(d) a condition under which at least some of the operations for a user for which the reference effect is higher than the target effect are given a higher priority than a priority determined without reference to the reference effect;
(e) a condition in which, when the target effect is within a first range that is neither a high effect zone nor a low effect zone, and the estimated risk is within a second range that is neither a high risk zone nor a low risk zone, a higher priority is given to at least a portion of the operations for users with a higher reference effect;
(f) a condition in which, when the target effect is within a third range for determining that the target effect is a low effect zone, and the estimated risk is within a second range that is neither a high risk zone nor a low risk zone, a higher priority is given to at least a portion of the operations for users with a higher reference effect;
(g) a condition under which at least some of the operations for a user with a lower reference effect are given a lower priority than would be determined if the reference effect were not referenced;
(h) a condition under which at least some of the operations for a user for whom the reference effect is lower than the target effect are given a lower priority than a priority determined without reference to the reference effect;
(i) a condition in which, if the target effect is within a first range that is neither a high effect zone nor a low effect zone, and the estimated risk is within a second range that is neither a high risk zone nor a low risk zone, a lower priority is given to at least some of the operations for users with a lower reference effect;
(j) a condition in which, when the target effect is within a third range for determining that the target effect is a low effect zone, and the estimated risk is within a second range that is neither a high risk zone nor a low risk zone, a lower priority is given to at least a portion of the operations for users with a lower reference effect;
method . Computer,
an effect estimation means for estimating an effect of a predetermined operation for a user for encouraging the user to perform a predetermined action on whether or not the user will perform the action, based on a causal score based on a difference between a statistical amount relating to a rate of execution of the action by users among a plurality of users who have received the operation and a statistical amount relating to a rate of execution of the action by users among the plurality of users who have not received the operation;
a risk estimation means for estimating a risk based on the probability that the user will not perform the action by using a model that outputs a risk index indicating the degree of risk based on the probability that the user will not perform the action in response to an input of one or more attributes related to the user;
A relationship specifying means for specifying a relationship between users based on a result of clustering based on values associated with the relationship between users;
a relationship strength determination means for determining a relationship strength indicating a closeness between the users based on an index indicating the strength of the relationship between the users in accordance with a judgment criterion corresponding to the relationship between the users;
A reference user specifying means for specifying a reference user having a relationship with the target user according to the relationship strength ;
a condition output means for outputting conditions regarding the operation for the target user based on data including a target effect , which is the effect estimated for the target user, a reference effect, which is the effect estimated for the reference user, and the risk, and for outputting at least one of the following conditions (a) to (j):
(a) a condition under which higher priority is given to at least some of the operations for users with higher target effectiveness, the operations for users with higher reference effectiveness, and the operations for users with higher estimated risk;
(b) a condition under which a lower priority is given to at least some of the operations for users with lower target effects, the operations for users with lower reference effects, and the operations for users with lower estimated risks;
(c) a condition under which at least some of the operations for users with a higher reference effect are given a higher priority than would be determined without reference to the reference effect;
(d) a condition under which at least some of the operations for a user for which the reference effect is higher than the target effect are given a higher priority than a priority determined without reference to the reference effect;
(e) a condition in which, when the target effect is within a first range that is neither a high effect zone nor a low effect zone, and the estimated risk is within a second range that is neither a high risk zone nor a low risk zone, a higher priority is given to at least a portion of the operations for users with a higher reference effect;
(f) a condition in which, when the target effect is within a third range for determining that the target effect is a low effect zone, and the estimated risk is within a second range that is neither a high risk zone nor a low risk zone, a higher priority is given to at least a portion of the operations for users with a higher reference effect;
(g) a condition under which at least some of the operations for a user with a lower reference effect are given a lower priority than would be determined if the reference effect were not referenced;
(h) a condition under which at least some of the operations for a user for whom the reference effect is lower than the target effect are given a lower priority than a priority determined without reference to the reference effect;
(i) a condition in which, if the target effect is within a first range that is neither a high effect zone nor a low effect zone, and the estimated risk is within a second range that is neither a high risk zone nor a low risk zone, a lower priority is given to at least some of the operations for users with a lower reference effect;
(j) a condition in which, when the target effect is within a third range for determining that the target effect is a low effect zone, and the estimated risk is within a second range that is neither a high risk zone nor a low risk zone, a lower priority is given to at least a portion of the operations for users with a lower reference effect;
program . Computer,
an effect estimation means for estimating an effect of a predetermined operation for a user for encouraging the user to perform a predetermined action on whether or not the user will perform the action, based on a causal score based on a difference between a statistical amount relating to a rate of execution of the action by users among a plurality of users who have received the operation and a statistical amount relating to a rate of execution of the action by users among the plurality of users who have not received the operation;
a risk estimation means for estimating a risk based on the probability that the user will not perform the action by using a model that outputs a risk index indicating the degree of risk based on the probability that the user will not perform the action in response to an input of one or more attributes related to the user;
A reference user specification means for specifying a reference user having a relationship with the target user based on graph data indicating the relationship between the users ;
a condition output means for outputting conditions regarding the operation for the target user based on data including a target effect , which is the effect estimated for the target user, a reference effect, which is the effect estimated for the reference user, and the risk, and for outputting at least one of the following conditions (a) to (j):
(a) a condition under which higher priority is given to at least some of the operations for users with higher target effectiveness, the operations for users with higher reference effectiveness, and the operations for users with higher estimated risk;
(b) a condition under which a lower priority is given to at least some of the operations for users with lower target effects, the operations for users with lower reference effects, and the operations for users with lower estimated risks;
(c) a condition under which at least some of the operations for users with a higher reference effect are given a higher priority than would be determined without reference to the reference effect;
(d) a condition under which at least some of the operations for a user for which the reference effect is higher than the target effect are given a higher priority than a priority determined without reference to the reference effect;
(e) a condition in which, when the target effect is within a first range that is neither a high effect zone nor a low effect zone, and the estimated risk is within a second range that is neither a high risk zone nor a low risk zone, a higher priority is given to at least a portion of the operations for users with a higher reference effect;
(f) a condition in which, when the target effect is within a third range for determining that the target effect is a low effect zone, and the estimated risk is within a second range that is neither a high risk zone nor a low risk zone, a higher priority is given to at least a portion of the operations for users with a higher reference effect;
(g) a condition under which at least some of the operations for a user with a lower reference effect are given a lower priority than would be determined if the reference effect were not referenced;
(h) a condition under which at least some of the operations for a user for whom the reference effect is lower than the target effect are given a lower priority than a priority determined without reference to the reference effect;
(i) a condition in which, if the target effect is within a first range that is neither a high effect zone nor a low effect zone, and the estimated risk is within a second range that is neither a high risk zone nor a low risk zone, a lower priority is given to at least some of the operations for users with a lower reference effect;
(j) a condition in which, when the target effect is within a third range for determining that the target effect is a low effect zone, and the estimated risk is within a second range that is neither a high risk zone nor a low risk zone, a lower priority is given to at least a portion of the operations for users with a lower reference effect;
program .

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