JP7572809B2 - Information Providing Device - Google Patents

Description

This disclosure relates to an information providing device.

Patent document 1 describes a device that detects the degree of congestion at each store and provides location information indicating stores (locations) whose degree of congestion is lower than a standard to a mobile terminal. After determining which stores have a lower degree of congestion than a standard, the device provides location information about the stores according to the user's preferences.

JP 2017-191500 A

According to the device described in Patent Document 1, location information of stores whose congestion level is above a certain level is not provided to the user. For example, even if a user does not mind waiting before entering a store, if the congestion level is above a certain level, the user cannot obtain location information of the store. Therefore, the device described in Patent Document 1 cannot provide the user with information about facilities that are appropriate for the user.

The present disclosure aims to provide an information providing device that can provide users with recommended information that is appropriate for the users.

An information providing device according to one aspect of the present disclosure includes a determination unit that selects recommendation candidates associated with candidate information that satisfies conditions acceptable to the user from among candidate information that is information about recommendation candidates based on user information including user characteristics, and determines a recommendation target from among the recommendation candidates, a generation unit that generates recommendation information about the recommendation target, and an output unit that outputs the recommendation information to a terminal device of the user.

In this information providing device, when the candidate information satisfies conditions acceptable to the user based on the user information, a recommendation target can be determined from the recommendation candidates associated with the candidate information. The recommendation target is determined based on the user information including the user's characteristics and the candidate information, and the recommendation information is output to the user's terminal device, so that information related to a recommendation target appropriate for the user can be provided to the user.

According to this disclosure, it is possible to provide users with recommended information that is appropriate for them.

FIG. 1 is a schematic diagram of an information providing system including an information providing device according to an embodiment. FIG. 2 is a diagram showing an example of the questionnaire. FIG. 3 is a block diagram showing a functional configuration of the information providing device shown in FIG. FIG. 4 is a diagram illustrating an example of group information. FIG. 5 is a diagram illustrating an example of user information. FIG. 6 is a diagram illustrating an example of store information. FIG. 7 is a diagram showing an example of visitor information. FIG. 8 is a flowchart showing in detail the setting process performed by the information providing device shown in FIG. FIG. 9 is a flowchart showing a series of processes of an information providing method performed by the information providing apparatus shown in FIG. FIG. 10 is a flowchart showing in detail the determination process shown in FIG. Fig. 11A is a diagram showing an example of a lock screen on a mobile terminal, Fig. 11B is a diagram showing another example of a lock screen on a mobile terminal, and Fig. 11C is a diagram showing yet another example of a lock screen on a mobile terminal. FIG. 12 is a diagram showing a hardware configuration of the information providing device shown in FIG.

Embodiments of the present disclosure will be described in detail below with reference to the accompanying drawings. Note that in the description of the drawings, the same elements are given the same reference numerals and duplicate descriptions will be omitted.

With reference to FIG. 1, the configuration of an information providing system including an information providing device according to one embodiment will be described. FIG. 1 is a schematic configuration diagram of an information providing system including an information providing device according to one embodiment. The information providing system 1 shown in FIG. 1 is a system that provides a user with recommendation information related to a recommendation target based on the characteristics of the user. A recommendation target is an object that is recommended to a user. A recommendation target is selected from recommendation candidates. A recommendation candidate is an object that can be recommended to a user. Examples of recommendation targets and recommendation candidates include, for example, commercial facilities such as stores. In this embodiment, the description will be given using stores as recommendation targets and recommendation candidates.

User characteristics include physical features, psychological tendencies, abilities, or unique qualities of the user that are expressed by a combination of these. In this embodiment, user characteristics refer to psychological tendencies. Psychological tendencies are also called psychological characteristics, biases, etc. Psychological tendencies are tendencies of thinking when humans act and make decisions. Examples of psychological tendencies include conformity bias, loss aversion bias, status quo bias, retention bias, time preference (present orientation bias), conformity effect, risk preference, Concorde effect, anchoring effect, and naivety.

Conformity bias is a psychological tendency to be swayed by the majority opinion. Loss aversion bias is a psychological tendency to avoid losses. Status quo bias is a psychological tendency to create psychological barriers against the unknown and stick to the current situation. Endowment bias is a psychological tendency to see great value in what one currently owns and feel reluctant to let go of it. Endowment bias can also mean a psychological tendency to think that possessing something rare is valuable. Time preference is a psychological tendency to prefer things that are closer to the present. Risk preference is a psychological tendency to be more likely to take risks. The conformity effect is a psychological tendency to want to be superior to others. The Concorde effect is a psychological tendency to be unable to make rational decisions because one does not want to waste the time and money that has been spent so far. The anchoring effect is a psychological tendency to distort judgments depending on the order in which information is obtained. Naivety is a psychological tendency in which plans and actual actions do not match.

The information provision system 1 includes a plurality of mobile terminals 10 (terminal devices), a plurality of store terminals 20, and an information provision device 30. The plurality of mobile terminals 10, the plurality of store terminals 20, and the information provision device 30 are configured to be able to communicate with each other via a network NW. The network NW may be configured as either a wired or wireless network. Examples of the network NW include a mobile communication network, the Internet, and a WAN (Wide Area Network).

The mobile terminal 10 is a device that is used by a user and can be carried by the user. Examples of the mobile terminal 10 include a smartphone and a tablet terminal. An application for receiving recommendation information (hereinafter referred to as a "recommendation application") is installed on the mobile terminal 10. When the user first uses (launches) the recommendation application, the user registers basic user information. Examples of the user basic information include gender and age. The recommendation application conducts a questionnaire for the user to estimate the user's psychological tendency (psychological characteristics). The questionnaire is conducted, for example, following the registration of the user basic information. The questionnaire includes questions for estimating the psychological tendency and answer options for the questions, each of which is prepared for each psychological tendency.

As shown in FIG. 2, for example, "You can receive 10,000 yen now. If you wait one week, you can receive a little more. How much would you need to receive in one week to be able to wait one week?" is prepared as a question Q1 for estimating time preference. For example, "1) 10,100 yen, 2) 10,500 yen, 3) 11,000 yen, 4) 12,000 yen, 5) 12,001 yen or more" are prepared as options for answer A1 to question Q1. For example, "When you go on a trip, how many tourist spots do you visit per day?" is prepared as a question Q2 for estimating loss aversion bias. For example, "1) 1 place, 2) 2 places, 3) 3 places, 4) 4 places, 5) 5 places or more" are prepared as options for answer A2 to question Q2. For example, question Q3 for estimating conformity bias may be prepared as follows: "We will ask 10 people, including yourself, to answer yes or no and raise their hand if the answer applies to them. You think it's no. If several other people raised their hands for yes, would you be influenced and raise your hand for yes?". For example, answer options A3 for question Q3 may be prepared as "1) Raise your hand for no, 2) 9 people, 3) 8 people, 4) 7 people, 5) 6 people."

Similar questions and answers are prepared for other biases. For example, a question for estimating ownership bias is prepared as "How many branded items do you own?". Response options for this question are prepared as "1) 0, 2) 1-2, 3) 3-4, 4) 5-6, 5) 7 or more." For example, a question for estimating conformity effect is prepared as "When working on content in which you compete with others, do you ever become motivated by getting a high ranking? Please select the answer that applies." Response options for this question are prepared as "1) I can enjoy it even if I'm ranked low, 2) It's not important to get a high ranking, but I would like to get there if I can, 3) I basically want to get a high ranking, 4) There's no doubt that getting a high ranking is a big motivation, and 5) Being motivated by getting a high ranking is what helps me concentrate the most."

The mobile terminal 10 transmits the user's basic information and information indicating the questionnaire responses to the information providing device 30. The user allows the recommendation application to switch the background image of the standby screen (lock screen). The background image is sometimes called wallpaper.

In the recommendation application, the user may set the acceptable crowdedness, acceptable cost, and acceptable distance as an example of user information. The acceptable crowdedness is the degree of crowdedness of a store that the user can tolerate. The acceptable cost is the cost that the user is willing to pay for the store. The acceptable distance is the distance that the user is willing to travel to the store. The acceptable crowdedness, acceptable cost, and acceptable distance may be set for each store type, store, or product. Products include services, rights, information, and the like. The user may set the acceptable crowdedness, acceptable cost, and acceptable distance at any time. For example, the user may set the acceptable crowdedness, acceptable cost, and acceptable distance when using (launching) the recommendation application for the first time. In response to the acceptable crowdedness, acceptable cost, and acceptable distance being set, the mobile terminal 10 transmits the acceptable crowdedness, acceptable cost, and acceptable distance to the information providing device 30.

Furthermore, the user may set preference information as an example of user information in the recommendation application. Preference information is information that indicates the user's preferences. For example, things that the user likes (for example, favorite foods) may be set as preference information. Things that the user dislikes (for example, disliked foods) may be set as preference information. The user may set preference information by selecting things that the user likes and dislikes from a list prepared in advance in the recommendation application. As preference information, a larger value may be assigned to a certain product or service as the preference information increases. The maximum value of preference is, for example, 1, and the minimum value of preference is, for example, 0. The user may set preference information at any timing. For example, the user may set preference information when using (launching) the recommendation application for the first time. In response to the preference information being set, the mobile terminal 10 transmits the preference information to the information providing device 30.

The mobile terminal 10 acquires location information (latitude and longitude) of the mobile terminal 10 using a GPS (Global Positioning System) or the like. The location information of the mobile terminal 10 may be user location information, which is the location information of the user. The mobile terminal 10 may acquire information on the installation location of a master station of a connected wireless network as location information. Examples of the installation location of a master station include a mobile network base station and a Wi-Fi access point. The mobile terminal 10 may acquire location information of another terminal existing in the vicinity of the mobile terminal 10 as location information of the mobile terminal 10. Examples of such terminals include a Bluetooth (registered trademark) beacon terminal. The mobile terminal 10 periodically transmits the location information to the information providing device 30 as user location information.

The store terminal 20 is a terminal device provided in each store and manages store information related to the store. Examples of the store terminal 20 include a desktop PC (Personal Computer), a notebook PC, a smartphone, and a tablet terminal. Details of the store information will be described later. A store clerk (manager) sets each piece of information included in the store information via the store terminal 20 at any time. Note that the description of the image may be set by selecting from descriptions prepared in advance. The store terminal 20 transmits the store information to the information providing device 30 every time the store information is updated.

The information providing device 30 is a device that provides recommendation information to a user who owns a mobile terminal 10. An example of the information providing device 30 is an information processing device such as a server device.

The functional configuration of the information providing device 30 will be described with reference to FIG. 3. FIG. 3 is a block diagram showing the functional configuration of the information providing device shown in FIG. 1. As shown in FIG. 3, the information providing device 30 functionally includes an acquisition unit 31, an analysis unit 32, a setting unit 33, an extraction unit 34, a determination unit 35, a generation unit 36, an output unit 37, a group information storage unit 41, a user information storage unit 42, a store information storage unit 43, and a visitor information storage unit 44.

The group information storage unit 41 is a functional unit that stores (stores) group information for each group. As shown in FIG. 4, the group information includes a group ID (identifier), attribute information, and group bias information. The group ID is information that can uniquely identify a group. The attribute information is information that indicates attributes that define a group. In the example shown in FIG. 4, gender and age are used as attributes. The group bias information is information that indicates the psychological tendency of a group, and includes a bias value for each psychological tendency. The bias value for a psychological tendency is a value that indicates the degree of the psychological tendency. In this embodiment, the larger the bias value, the stronger the psychological tendency. The minimum value of the bias value for a psychological tendency is 0, and the maximum value of the bias value for a psychological tendency is 1. The bias value for each psychological tendency included in the group bias information is, for example, the average value of the bias values for the psychological tendencies of users belonging to the group.

The user information storage unit 42 is a functional unit that stores (stores) user information for each user. As shown in FIG. 5, the user information includes a terminal ID, user basic information, preference information, bias information, behavior change rate, acceptable congestion level, acceptable cost, and acceptable distance. The terminal ID is information that can uniquely identify the mobile terminal 10. The bias information is information that indicates the psychological tendency of the user, and includes a bias value for each psychological tendency.

The user's behavior change rate is set for each recommended store and indicates the percentage of times that a user acts according to the recommended information when the user receives the recommended information. The behavior change rate for a store is, for example, the percentage of the number of times the user went to the store according to the recommended information relative to the number of times the user received the recommended information for the store. In other words, the behavior change rate for a store is calculated by dividing the number of times the user acted according to the recommended information for the store by the number of times the user received the recommended information for the store. The terminal ID, user basic information, preference information, bias information, behavior change rate, acceptable congestion level, acceptable cost, and acceptable distance are associated with each other, so the user basic information, preference information, bias information, behavior change rate, acceptable congestion level, acceptable cost, and acceptable distance are information of the user who possesses the mobile terminal 10 identified by the terminal ID.

The store information storage unit 43 is a functional unit that stores (stores) store information (an example of candidate information) for each store. As shown in FIG. 6, the store information includes a store ID, a store name, store location information (an example of candidate location information), a congestion level, a candidate cost, access point information, an image, a description of the image, and a recommendation level. The store ID is information that can uniquely identify a store. The store name is the name of the store. The store location information includes the location information (latitude and longitude) of the store. The store location information is obtained using map information, etc. The congestion level is an index that indicates how crowded the store is. In this embodiment, the larger the congestion level, the more crowded the store is. For example, if the number of people in the store is the same as the number of people that the store can accommodate (for example, the number of seats), the congestion level is 1.0. The candidate cost indicates a feature of the price paid for a product or service at the store. The candidate cost is, for example, the average price paid at the store. The access point information is information that can identify the access point located closest to the store. For ease of explanation, Figure 6 shows the locations where the access points are installed.

The images are images related to the store. Examples of images include product images, which are images of products offered by the store. The images can be used as background images for the mobile terminal 10. Therefore, an image suitable as a background image can be adopted. The store information includes one or more images. A description of the image is set for each image, such as the name, type, and features of the product included in the image. A recommendation level is set for each image, and is an index showing the degree to which the store recommends the image. A higher recommendation level indicates a stronger recommendation by the store.

The store visitor information storage unit 44 is a functional unit that stores (stores) store visitor information for each store. The store visitor information is information about users (hereinafter referred to as "store visitors") who actually visited a store in accordance with the recommendation information. As shown in FIG. 7, the store visitor information includes a store ID, bias information, and a behavior change rate. The bias information is information indicating the psychological tendencies of all store visitors to the store indicated by the store ID, and includes a bias value for each psychological tendency. The bias value for each psychological tendency is, for example, the average bias value of the psychological tendencies of all store visitors. The behavior change rate is set for each store to be recommended, and indicates the proportion of all store visitors to the store indicated by the store ID who change their behavior. Each behavior change rate is, for example, the average behavior change rate of all store visitors. Note that the store visitor information may include, for each image included in the store information for each store, a bias value for the psychological tendencies of store visitors who changed their behavior as a result of the image being recommended.

The acquisition unit 31 is a functional unit that acquires various information. For example, the acquisition unit 31 acquires information indicating a terminal ID, user basic information, and questionnaire responses from the mobile terminal 10 of a new user. For example, the acquisition unit 31 acquires a terminal ID and user location information from the mobile terminal 10. For example, the acquisition unit 31 acquires store information from a store terminal 20. The acquisition unit 31 outputs the store information to the store information storage unit 43 and stores it in the store information storage unit 43.

The analysis unit 32 is a functional unit that analyzes the psychological tendency of each of the multiple groups and generates group information for each of the multiple groups. The analysis unit 32 calculates a psychological tendency bias value for each psychological tendency for each of the multiple users based on information indicating the questionnaire responses obtained from the mobile terminal 10. For each group, the analysis unit 32 calculates the average value of the psychological tendency bias values of the users belonging to the group for each psychological tendency, and sets this as the psychological tendency bias value of the group. In this manner, the analysis unit 32 generates group information. The analysis unit 32 outputs the group information to the group information storage unit 41 and stores it in the group information storage unit 41.

The setting unit 33 is a functional unit that sets the user information of the new user. The setting unit 33 sets the terminal ID and user basic information received from the new user's mobile terminal 10 to the terminal ID and user basic information of the new user. The setting unit 33 identifies a group to which the new user belongs from among multiple groups based on the user basic information of the new user, and sets the user information of the new user using the group information of the identified group. The method of setting the user information will be described in detail later. The setting unit 33 outputs the user information of the new user to the user information storage unit 42 and stores it in the user information storage unit 42.

The extraction unit 34 is a functional unit that extracts stores that may be recommended. The extraction unit 34 extracts stores that are located in the vicinity of the mobile terminal 10.

The determination unit 35 is a functional unit that determines, based on the user information, as a recommendation target, a store associated with store information that satisfies conditions acceptable to the user. The determination unit 35 determines a recommendation target based on at least one of the bias values of psychological tendencies included in the bias information of the user information. Specifically, the determination unit 35 selects recommendation candidates based on parameters set based on the bias values of psychological tendencies, and determines a recommendation target from among the recommendation candidates. The parameters will be described later.

The determination unit 35 determines an index to be used for selecting a recommendation candidate from among the multiple stores extracted by the extraction unit 34 from among the allowable congestion level, the allowable cost, and the allowable distance, based on the magnitude of the values of the above parameters. The determination unit 35 performs at least one of a first selection process for selecting a recommendation candidate based on the allowable congestion level, a second selection process for selecting a recommendation candidate based on the allowable cost, and a third selection process for selecting a recommendation candidate based on the allowable distance. That is, the determination unit 35 may perform any one of the first selection process, the second selection process, and the third selection process, or may perform a combination of multiple selection processes. The determination unit 35 selects a recommendation candidate from the store candidates by performing at least one of the first selection process, the second selection process, and the third selection process. The determination unit 35 determines a recommendation target from the recommendation candidates. Details of the first selection process, the second selection process, and the third selection process, and details of the method for determining a recommendation target will be described later.

The generation unit 36 is a functional unit that generates recommendation information related to a recommendation target. In this embodiment, the generation unit 36 generates recommendation information related to a store that is a recommendation target determined by the determination unit 35. The recommendation information includes an image related to the store. The image may be a product image of a product provided by the store. The generation unit 36 generates recommendation information based on at least one of the user information and the store information. For example, the generation unit 36 selects an image for recommendation from among images included in the store information based on the recommendation degree of an image included in the store information of the store determined by the determination unit 35. If preference information is included in the user information, the generation unit 36 may select an image for recommendation further based on the preference information. The generation unit 36 generates recommendation information including an image for recommendation.

The generation unit 36 may generate recommendation information according to the psychological tendency of the user. For example, the generation unit 36 may generate a message according to the psychological tendency of the user, and generate recommendation information including a recommendation image and a message. The message is, for example, a message that induces a user to take action.

The generation unit 36 may generate recommendation information based on visitor information (an example of behavioral information) of a visitor (user) who acts in accordance with the recommendation information, and user information of a user to whom the recommendation information is to be provided. Details will be described later.

The output unit 37 is a functional unit that outputs recommendation information. The output unit 37 outputs (transmits) the recommendation information to the mobile terminal 10 together with an instruction to set the image included in the recommendation information to the mobile terminal 10 as a background image on the lock screen of the mobile terminal 10. When the recommendation information includes a message, the output unit 37 may further output an instruction to display the message superimposed on the background image.

Next, the user information setting process performed by the information providing device 30 will be described with reference to FIG. 8. FIG. 8 is a flowchart showing in detail the user information setting process performed by the information providing device shown in FIG. 1. The process shown in FIG. 8 is started, for example, when the user uses a recommendation application for the first time, by inputting basic user information, responses to a questionnaire, etc.

As shown in FIG. 8, first, the acquisition unit 31 acquires (receives) the terminal ID, the user basic information, and information indicating the questionnaire responses from the mobile terminal 10 (step S11). Then, the acquisition unit 31 outputs the user basic information and the information indicating the questionnaire responses to the analysis unit 32, and outputs the terminal ID and the user basic information to the setting unit 33.

Next, the analysis unit 32 generates group information (step S12). The method of generating group information will be specifically described below. When the analysis unit 32 receives information indicating the user basic information and the answers to the questionnaire from the acquisition unit 31, the analysis unit 32 calculates the bias value of the psychological tendency for each psychological tendency based on the information indicating the answers to the questionnaire obtained from the mobile terminal 10. For example, the analysis unit 32 calculates the bias value of the time preference so that the bias value of the time preference becomes larger as the amount (number of options) of the answer A1 shown in FIG. 2 becomes larger. The analysis unit 32 calculates the bias value of the loss aversion bias so that the bias value of the loss aversion bias becomes larger as the number (number of options) of the answer A2 shown in FIG. 2 becomes larger. The analysis unit 32 calculates the bias value of the conformity bias so that the bias value of the conformity bias becomes larger as the number of options of the answer A3 shown in FIG. 2 becomes larger. The analysis unit 32 calculates the bias values of the other psychological tendencies in a similar manner.

Then, the analysis unit 32 identifies a group to which the user who owns the mobile terminal 10 belongs, based on the user basic information. For example, if the user is a 35-year-old man, the analysis unit 32 identifies group M3, which is a group of men in their 30s. Then, for the identified group, the analysis unit 32 calculates the average value of the psychological tendency bias values of the users who belong to the group for each psychological tendency, and sets this as the psychological tendency bias value of the group. In this manner, the analysis unit 32 generates group information. Then, the analysis unit 32 outputs the group information to the group information storage unit 41, and stores it in the group information storage unit 41.

Then, the setting unit 33 sets the user information of the new user (step S13). Specifically, when the setting unit 33 receives the terminal ID and the user basic information from the acquisition unit 31, the setting unit 33 sets the terminal ID and the user basic information received from the acquisition unit 31 to the terminal ID and the user basic information of the user information of the mobile terminal 10. Then, the setting unit 33 identifies the group to which the user who owns the mobile terminal 10 belongs from among multiple groups based on the user basic information, and sets the bias information of the new user using the group information of the identified group. For example, if the user is a 35-year-old man, the setting unit 33 acquires the group information of group M3 from the group information stored in the group information storage unit 41, and sets the group bias information included in the group information of group M3 to the bias information of the user information of the user.

In this manner, the setting unit 33 sets (generates) the user information of the new user. Note that an invalid value is set for each behavioral change rate in the user information. The setting unit 33 then outputs the user information of the new user to the user information storage unit 42 and stores it in the user information storage unit 42. This completes the user information setting process.

The analysis unit 32 generates group information in advance based on information indicating survey responses obtained from multiple users (mobile terminals 10). Therefore, the analysis unit 32 does not need to generate (update) group information every time a new user uses a recommended application for the first time. In this case, step S12 is omitted.

Next, the information providing method performed by the information providing device 30 will be described with reference to Figs. 9, 10, and Figs. 11(a) to 11(c). Fig. 9 is a flowchart showing a series of processes of the information providing method performed by the information providing device shown in Fig. 1. Fig. 10 is a flowchart showing the decision process shown in Fig. 9 in detail. Fig. 11(a) is a diagram showing an example of a lock screen on a mobile terminal. Fig. 11(b) is a diagram showing another example of a lock screen on a mobile terminal. Fig. 11(c) is a diagram showing yet another example of a lock screen on a mobile terminal. The series of processes shown in Fig. 9 is started, for example, by the user starting a recommended application. The mobile terminal 10 running the recommended application periodically transmits the terminal ID and user position information to the information providing device 30.

As shown in FIG. 9, first, the acquisition unit 31 acquires the terminal ID and user location information of the mobile terminal 10 (step S21). Then, the acquisition unit 31 outputs the terminal ID and user location information to the extraction unit 34.

Then, the extraction unit 34 extracts stores (step S22). Specifically, when the extraction unit 34 receives the terminal ID and user location information from the acquisition unit 31, the extraction unit 34 acquires multiple pieces of store information around the location indicated by the user location information from the multiple pieces of store information stored in the store information storage unit 43. That is, the stores of the acquired store information are extracted. Then, the extraction unit 34 outputs the terminal ID and the store information of the multiple extracted stores to the determination unit 35. Note that, if the user location information is information on the installation location of an access point, the extraction unit 34 acquires multiple pieces of store information including access point information indicating the installation location of the access point from the multiple pieces of store information stored in the store information storage unit 43.

Next, when the determination unit 35 receives the terminal ID and the store information of the extracted stores from the extraction unit 34, the determination unit 35 performs a determination process to determine a store to be recommended from the extracted stores (step S23). In the determination process of step S23, as shown in FIG. 10, the determination unit 35 first acquires user information and store visitor information (step S31). The determination unit 35 acquires user information including the terminal ID received from the extraction unit 34 from the multiple pieces of user information stored in the user information storage unit 42. The determination unit 35 acquires store visitor information including the store ID in the store information received from the extraction unit 34 from the multiple pieces of store visitor information stored in the store visitor information storage unit 44.

Then, the determination unit 35 sets parameters for determining whether or not to execute the first selection process, the second selection process, and the third selection process based on the bias value of the psychological tendency included in the user information (step S32). The parameters include a congestion parameter, a cost parameter, and a distance parameter. The congestion parameter is calculated using time preference, loss aversion bias, and conformity bias. The value of the congestion parameter is, for example, the average value of the bias value of the time preference, the bias value of the loss aversion bias, and the bias value of the conformity bias. The cost parameter is calculated using the loss aversion bias and the conformity effect. The value of the cost parameter is, for example, the average value of the bias value of the loss aversion bias and the bias value of the conformity effect. The distance parameter is calculated using the time preference and loss aversion bias. The value of the distance parameter is, for example, the average value of the bias value of the time preference and the bias value of the loss aversion bias. Note that these parameters may be calculated using one bias value or four or more bias values included in the user information.

Then, the decision unit 35 decides whether to execute the first selection process, the second selection process, and the third selection process based on the set congestion parameter, cost parameter, and distance parameter (step S33). For example, if the congestion parameter has the highest value among the congestion parameter, cost parameter, and distance parameter, the decision unit 35 decides to execute the first selection process. If the cost parameter has the highest value, the decision unit 35 decides to execute the second selection process. If the distance parameter has the highest value, the decision unit 35 decides to execute the third selection process. If the values of two or more parameters are the same, the decision unit 35 decides to execute the selection process associated with any one of the parameters with the same value.

Next, the determination unit 35 executes the selection process determined in step S33 (step S34). In the first selection process, the determination unit 35 uses the allowable crowdedness and the crowdedness of each store. Specifically, the determination unit 35 selects, from among the extracted multiple stores, a store associated with store information including a crowdedness less than the user's allowable crowdedness as a recommendation candidate. In the second selection process, the determination unit 35 uses the allowable cost and the candidate cost of each store. Specifically, the determination unit 35 selects, from among the extracted multiple stores, a store associated with store information including a candidate cost less than the user's allowable cost as a recommendation candidate. In the third selection process, the determination unit 35 calculates a candidate distance based on the user location information and the store location information. The candidate distance is the distance from the user's location to the store's location. In the third selection process, the determination unit 35 uses the allowable distance and the candidate distance of each store. Specifically, the determination unit 35 selects, from among the extracted stores, a store associated with store information whose candidate distance is less than the user's acceptable distance, as a recommendation candidate.

The determination unit 35 may further execute an unexecuted process among the first selection process, the second selection process, and the third selection process. If no recommendation candidates exist in step S34, the determination unit 35 returns to step S33, determines a selection process other than the already determined selection process, and then executes the newly determined selection process again in step S34.

Next, the determination unit 35 selects at least one psychological tendency bias value from the multiple psychological tendency bias values included in the user information (step S35).

Next, the determination unit 35 calculates the similarity of each recommendation candidate selected in step S34 using the bias value of the psychological tendency selected in step S35 (step S36). Specifically, the determination unit 35 generates a user vector having as elements the bias value of the psychological tendency selected in step S35 and the behavior change rate included in the user information. Furthermore, the determination unit 35 generates a store vector having as elements the bias value of the psychological tendency corresponding to the bias selected in step S35 and the behavior change rate included in the store visitor information for each recommendation candidate. Then, the determination unit 35 calculates the similarity between the user vector and the store vector of each recommendation candidate. Cosine similarity may be used as the similarity. In this case, the determination unit 35 normalizes each vector and calculates the cosine similarity using the normalized vector.

Then, the determination unit 35 determines a store to be recommended based on the similarity of each recommendation candidate (step S37). For example, the determination unit 35 determines the recommendation candidate having the greatest similarity from among the recommendation candidates as the store to be recommended. The determination unit 35 then outputs store information of the store to be recommended together with the user information to the generation unit 36. Note that when one recommendation candidate is selected by the selection process, the determination unit 35 may determine the recommendation candidate as the store to be recommended. In this case, the selection of parameters (step S35) and the calculation of similarity (step S36) may not be performed.

A method for determining a store to be recommended will be described using a specific example. Assume that stores A, B, C, and D are extracted, and the user information includes a bias value of time preference, a bias value of loss aversion bias, a bias value of conformity bias, and a bias value of conformity effect. The bias value of time preference is 0.8, the bias value of loss aversion bias is 0.6, the bias value of conformity bias is 0.7, and the bias value of conformity effect is 0.6. At this time, the value of the congestion parameter is 0.7, the value of the cost parameter is 0.6, and the value of the distance parameter is 0.7. Since the values of the congestion parameter and the distance parameter are the largest, when the congestion parameter is selected as one bias, the first selection process is executed by the determination unit 35. For example, the allowable congestion degree included in the user information is 0.9. The congestion degree of store A is 0.3, the congestion degree of store B is 0.4, the congestion degree of store C is 0.7, and the congestion degree of store D is 1.1. Stores with a congestion level equal to or lower than the allowable congestion level are selected as recommendation candidates to which customers are to be sent, so the determination unit 35 selects stores A, B, and C.

Then, customer information for store A, store B, and store C is obtained. For ease of explanation, the customer information is expressed as a store vector including the elements [time preference bias value, conformity bias bias value, behavior change rate for store A, behavior change rate for store B, behavior change rate for store C]. The store vector for store A is [0.8, 0.2, 0.6, 0.0, 0.1], the store vector for store B is [0.9, 0.6, 0.5, 0.2, 0.0], and the store vector for store C is [0.2, 0.5, 0.2, 0.1, 0.5]. Meanwhile, the user vector of the user information corresponding to the store vector is [0.8, 0.7, 0.4, 0.1, 0.1].

In step S35, when the bias value of time preference is selected, the similarity (cosine similarity) of each store is calculated using the vector obtained by removing the bias value of the synchronization bias from each of the above vectors. In this case, the similarity of store A is 0.878, the similarity of store B is 0.984, and the similarity of store C is 0.739. Store B, which has the highest similarity among stores A, B, and C, is determined to be the store to be recommended.

Then, the generation unit 36 generates recommendation information (step S24). In step S24, when the generation unit 36 receives the store information and the user information from the determination unit 35, the generation unit 36 selects an image for recommendation from among the images included in the store information based on the recommendation level of the image included in the store information and the preference information included in the user information.

The generating unit 36 assigns a weight to each recommendation level based on, for example, the preference information. For example, the generating unit 36 assigns a weight greater than the default value to the recommendation level of an image that corresponds to a favorite item set in the preference information among images included in the store information. The default value is, for example, 1. The generating unit 36 assigns a default weight to the recommendation levels of other images. The generating unit 36 corrects the recommendation level by multiplying the recommendation level of each image by the weight. If a disliked item is set in the preference information, the generating unit 36 may exclude images of the disliked item (for example, a weight of 0). The generating unit 36 may exclude images that have been selected as recommended images in the past (for example, a weight of 0).

Then, the generation unit 36 uses the corrected recommendation level to select an image for recommendation from among the images included in the store information. The generation unit 36 may select an image with the highest recommendation level as the image for recommendation. The generation unit 36 may randomly select an image for recommendation from among images with a recommendation level equal to or higher than a preset recommendation threshold.

A method for selecting an image for recommendation will be explained using a specific example. Assume that "seafood bowl" is set as a favorite item in the preference information, and "beef bowl" is set as a dislike item in the preference information. Assume that the images included in the store information are [beef bowl, new product, seafood bowl, char siu bowl], each recommendation level is [0.5, 0.9, 0.7, 0.4], and the weight assigned to the favorite items is set to 1.5. In this case, the preference information gives [beef bowl, new product, seafood bowl, char siu bowl] = [0, 0.9, 1.05, 0.4]. The generation unit 36 then selects the image of the seafood bowl with the highest recommendation level as the image for recommendation. If the recommendation threshold is set to 0.7, the generation unit 36 may select either the image of the new product or the image of the seafood bowl as the image for recommendation.

When the customer information includes, for each image included in the store information of each store, a bias value of the psychological tendency of a customer who has changed his/her behavior after the image was recommended, the generation unit 36 may generate recommendation information based on the customer information of the customer and the user information of the user to whom the recommendation information is to be provided. The generation unit 36 acquires the bias value of the psychological tendency of a customer who has changed his/her behavior after the image included in the store information was recommended. Then, the generation unit 36 generates, for each image, a customer vector having as elements the average value of each bias value of the psychological tendency of the customer who has changed his/her behavior. The generation unit 36 generates a user vector having as elements each bias value of the psychological tendency of the user.

Then, the generation unit 36 calculates the similarity between the visitor vector and the user vector of each image. Cosine similarity may be used as the similarity. In this case, the generation unit 36 normalizes each vector and calculates the cosine similarity using the normalized vector. The generation unit 36 multiplies the similarity for each image by the recommendation level corresponding to the image. An image corresponding to the element that has the maximum value as a result of this process is selected. Note that the generation unit 36 may select an image corresponding to the element that has the maximum similarity without multiplying the recommendation level of the image included in the store information by the above similarity.

A method for selecting an image for recommendation when customer information is used will be explained using a specific example. Assume that the images included in the store information are [soy sauce ramen, miso ramen, salt ramen]. Assume that the cosine similarity between the bias value of the psychological tendency of the customer and the bias value of the psychological tendency of the user for each image included in the store information is [0.5, 0.8, 0.2]. Note that the cosine similarity is calculated in the same manner as the calculation method described above. When the recommendation degree for each image [soy sauce ramen, miso ramen, salt ramen] included in the store information is [1.0, 0.7, 0.4], multiplying it by the cosine similarity gives [soy sauce ramen, miso ramen, salt ramen] = [0.5, 0.56, 0.08]. Then, the generation unit 36 selects the image of miso ramen with the highest recommendation degree as the image for recommendation.

Then, the generating unit 36 generates recommendation information including an image for recommendation, and outputs the recommendation information to the output unit 37. The generating unit 36 may generate a message according to the psychological tendency determined. The generating unit 36 determines the psychological tendency having the maximum bias value among the bias values of the psychological tendency included in the user information. For example, when the psychological tendency is time preference, the generating unit 36 generates a message indicating that the time can be shortened. An example of such a message is "You can enter the popular xx without waiting now!". When the psychological tendency is conformity bias, the generating unit 36 generates a message indicating that a large number of people are visiting. Examples of such a message are "When you think of Osaka, you think of xx!" and "Why not try the popular xx?". When the psychological tendency is loss aversion bias, the generating unit 36 generates a message indicating that a loss can be avoided or a profit can be obtained. Examples of such a message are "We are distributing xx coupons for a limited time only" and "If you go to xx, you will receive d points!". If the maximum psychological tendency bias value is smaller than a predetermined threshold (e.g., 0.5), the generation unit 36 may generate the store name as a message. The generation unit 36 may generate recommendation information including a recommendation image and a message, and output the recommendation information to the output unit 37.

Then, the output unit 37 outputs the recommendation information (step S25). Specifically, when the output unit 37 receives the recommendation information from the generation unit 36, the output unit 37 outputs (transmits) the recommendation information to the mobile terminal 10 together with a command to set the image included in the recommendation information to the mobile terminal 10 as a background image on the lock screen of the mobile terminal 10. If the recommendation information includes a message, the output unit 37 further outputs a command to display the message superimposed on the background image. The output unit 37 may output the message by push communication. Furthermore, the output unit 37 may register the recommendation information in a history (log information) of the recommendation information (not shown).

When the mobile terminal 10 receives recommendation information from the information providing device 30, it sets the image included in the recommendation information as the background image on the lock screen, and displays the message included in the recommendation information superimposed on the background image. FIG. 11(a) shows an example of recommendation information displayed on the mobile terminal 10 of a user who has a time preference. FIG. 11(b) shows an example of recommendation information displayed on the mobile terminal 10 of a user who has a conformity bias. FIG. 11(c) shows an example of recommendation information displayed on the mobile terminal 10 of a user who does not have a specific psychological tendency. As shown in FIG. 11(a) to (c), recommendations may be made according to the user's psychological tendency.

With the above, the series of processes of the information provision method is completed. The first selection process, the second selection process, and the third selection process may all be performed by repeating the step of determining a selection process (step S33) in the above-mentioned flowchart, and the step of executing the selection processes (step S34). For example, in step S33, the determination unit 35 may determine the order in which to execute the first selection process, the second selection process, and the third selection process in descending order of the congestion degree parameter, the cost parameter, and the distance parameter. The determination unit 35 does not have to determine to execute all of the first selection process, the second selection process, and the third selection process.

In the information providing device 30 described above, a store associated with store information that satisfies the conditions acceptable to the user based on the user information is selected as a recommendation candidate, and a recommendation target is determined from the recommendation candidates. Recommendation information related to the recommendation target is generated by the generation unit 36, and the recommendation information is output to the user's mobile terminal 10 by the output unit 37. For example, some users try to avoid crowded stores, while other users may visit a store even if it is somewhat crowded. As in this example, acceptable conditions may differ for each user. For example, if recommendation candidates are selected based on a threshold value that is uniformly determined for all users without considering the conditions acceptable to the user, stores with conditions acceptable to the user may be excluded from the recommendation candidates, and appropriate recommendation information for the user may not be provided. If recommendation candidates are selected based on a threshold value that is uniformly determined for all users, stores with conditions that the user cannot accept may be included in the recommendation candidates. Even if recommendation information related to such a store is provided to the user, the user is unlikely to visit the store. In the information providing device 30 of this embodiment, stores associated with store information that meets the conditions acceptable to the user are selected as recommendation candidates, and a recommended store is determined from the recommendation candidates, so that the possibility that the user will visit the recommended store can be increased. Therefore, the information providing device 30 can provide the user with recommended information that is appropriate for the user.

The store information includes the degree of congestion of the store, and the acceptable conditions include the allowable congestion degree, which is the degree of congestion that the user can tolerate, and the determination unit 35 selects recommendation candidates associated with store information including a congestion degree less than the user's allowable congestion degree. For example, some users do not mind waiting before entering the store, while other users want to avoid waiting. If only stores with a congestion degree less than a threshold value set uniformly for all users are selected as recommendation candidates, stores that are more crowded than the threshold value are excluded from the recommendation candidates, so that appropriate recommendation information may not be provided to users who do not mind waiting. According to the above configuration, recommendation candidates are selected according to the degree of congestion that the user can tolerate. For example, compared to a case where a store with a congestion degree less than a threshold value set uniformly in advance is recommended, if the user's allowable congestion degree is greater than the threshold value, the store can be included in the recommendation candidates even if the store has a congestion degree equal to or greater than the threshold value. This increases the likelihood that the store will be selected as a recommended target, further increasing the likelihood that the user will visit the recommended store, and increasing the likelihood that the store will increase sales. Unlike the above example, even if the user's acceptable level of congestion is below the threshold level of congestion, it is possible to provide information about stores with an appropriate level of congestion for the user, thereby reducing psychological burdens such as stress caused by the time it takes for the user to enter the store.

The store information includes candidate costs, which are costs related to the object provided by the store, and the acceptable conditions include allowable costs, which are costs that the user can tolerate paying, and the determination unit 35 selects recommendation candidates associated with store information including candidate costs less than the user's allowable costs. For example, some users do not mind if the cost of spending at a store is relatively high, while other users want to keep costs to a minimum. If only stores less than a cost threshold set uniformly for all users are selected as recommendation candidates, stores with costs higher than the threshold are excluded from the recommendation candidates, so that users who do not mind high costs may not be provided with appropriate recommendation information. According to the above configuration, recommendation candidates are selected according to the degree of congestion that the user can tolerate. For example, compared to a case where a store less than a uniform cost threshold set in advance is recommended, if the user's allowable cost is higher than the threshold, the store can be included in the recommendation candidates even if the expected cost of spending at the store is equal to or higher than the threshold. Therefore, the possibility that the store will be selected as a recommended target increases, further increasing the possibility that the user will visit the recommended store, and increasing the possibility that the store will increase sales. Also, in the above example, the number of recommendation candidates increases, so the possibility of sending the user to many stores increases. Unlike the above example, even if the user's acceptable cost is below the above cost threshold, it is possible to provide information on stores with appropriate costs for the user, thereby reducing unexpected financial burdens on the user.

The device further includes an acquisition unit 31 that acquires user location information, which is user location information, and the candidate information includes candidate location information, which is location information of the recommendation candidate, and the acceptable condition includes an acceptable distance, which is a distance that the user can tolerate moving to, and the determination unit 35 selects a recommendation candidate whose candidate distance, which is the distance from the user to the store calculated based on the user location information and the store location information, is less than the acceptable distance of the user. For example, some users do not mind the distance to the store being relatively far away, while other users prefer the store to be nearby. If only stores that are less than a distance threshold that is uniformly set for all users are selected as recommendation candidates, stores whose distance is greater than or equal to the threshold are excluded from the recommendation candidates, so that appropriate recommendation information may not be provided to users who do not mind the distance being far. According to the above configuration, recommendation candidates are selected according to the distance that the user can tolerate. For example, compared to recommending stores that are less than a preset uniform distance threshold, if the user's tolerable distance is greater than the threshold, the store can be included in the recommendation candidates even if the store is far away and the distance is greater than the threshold. This increases the likelihood that the store will be determined as a recommended target, further increasing the likelihood that the user will visit the recommended store, and increasing the likelihood that the store will increase sales. Furthermore, if the user's tolerable distance is greater than the above threshold, it is possible to increase the likelihood that the user will be sent to a wide range of locations. Unlike the above example, even if the user's tolerable distance is less than the above distance threshold, it is possible to provide information about stores that are an appropriate distance for the user, thereby reducing unexpected physical strain on the user.

The generation unit 36 generates recommendation information based on at least one of the user information and the store information. According to this configuration, the recommendation information (image) provided to the user's mobile terminal 10 is selected, for example, taking into consideration the user's preferences contained in the user information or the recommendation level of the store contained in the store information. For example, when generating recommendation information based on user information, the recommendation information is selected according to the user's preferences, which further increases the possibility that the user will visit a recommended store that matches the preferences. For example, when generating recommendation information based on store information, recommendation information with a high recommendation level for the store is selected, allowing the store to advertise the highly recommended information to the user.

The generating unit generates recommendation information based on the visitor information of the visitor who acted according to the recommendation information and the user information of the user to whom the recommendation information is to be provided. According to this configuration, for example, in the present embodiment, when the visitor information includes the bias value of the psychological tendency of the visitor who acted according to the recommendation information and the user information includes the bias value of the psychological tendency of the user, the recommendation information to be provided to the user's mobile terminal 10 is selected taking into consideration each bias value. For example, the higher the similarity between the bias value of the psychological tendency of the visitor who acted according to the recommendation information and the bias value of the psychological tendency of the user, the higher the possibility that the user will act according to the recommendation information in the same way as the visitor who acted according to the recommendation information. Therefore, even if the user information does not include preference information related to the recommendation information, the possibility that the user will visit the recommended store can be further increased by providing recommendation information that led a visitor with a high similarity to visit the store.

Although the embodiments of the present disclosure have been described above, the present disclosure is not limited to the above embodiments.

The information providing device 30 may be configured as a single device that is physically or logically coupled, or may be configured as multiple devices that are physically or logically separated from each other. For example, the information providing device 30 may be realized by multiple computers distributed over a network, such as in cloud computing. As described above, the configuration of the information providing device 30 may include any configuration that can realize the functions of the information providing device 30.

The recommendation target is not limited to commercial facilities such as stores. The recommendation target may be a facility other than a commercial facility. The recommendation target may be a place, product, etc. that can be recommended to the user.

In the above embodiment, a store clerk (manager) sets the store's congestion level and the proposed cost via the store terminal 20. In this case, since there is a possibility that the store clerk will forget to set the congestion level and the proposed cost, the default value for the congestion level may be set to a value indicating congestion (e.g., 1.0), and the default value for the proposed cost may be set to a value indicating cost (e.g., 1000). Since there is a possibility that the store clerk will forget to update the congestion level, the congestion level may be updated to a value indicating congestion (e.g., 1.0) if a certain amount of time has passed since the last update.

The information providing device 30 may calculate the degree of congestion of a store. For example, when the location indicated by the location information of the mobile terminal 10 is included within the store, the information providing device 30 determines that the user of the mobile terminal 10 has entered the store and estimates the number of users present in the store. The information providing device 30 may then calculate the degree of congestion by dividing the estimated number of people by the number of people the store can accommodate. When an access point is installed in the store, the information providing device 30 may calculate the degree of congestion by dividing the number of mobile terminals 10 captured by the access point by the result of multiplying the number of people the store can accommodate by the line share rate.

In the above embodiment, the group is defined by attributes, but may be defined by a bias value of psychological tendency. For example, a set of users having a time preference of 0.8 or more may be defined as a group. The group information may further include a behavior change rate. The behavior change rate of the group information is, for example, the average value of the behavior change rates of users belonging to the group. In this case, the determination unit 35 may determine the store to be recommended using the group information of the group to which the user belongs, instead of the user information.

At least one of the allowable congestion level, allowable cost, and allowable distance may not be set in the recommendation application. For example, the group information may include the allowable congestion level, allowable cost, and allowable distance. The setting unit 33 may set an index (any of the allowable congestion level, allowable cost, and allowable distance) of the allowable congestion level, allowable cost, and allowable distance that is not set by the user, using an index (the allowable congestion level, allowable cost, and allowable distance) included in the group information of the group to which the user belongs. The allowable congestion level may be set based on the value of a congestion level parameter. The allowable cost may be set based on the value of a cost parameter. The allowable distance may be set based on the value of a distance parameter.

The determination unit 35 may not select the first selection process, the second selection process, or the third selection process. In this case, the determination unit 35 selects, as a recommendation candidate, a store associated with store information that satisfies at least one of the conditions, such as less than the allowable congestion level, less than the allowable cost, and less than the allowable distance.

In the above embodiment, the generation unit 36 selects an image for recommendation based on the recommendation level of the image included in the store information and the preference information included in the user information, but the generation unit 36 may select an image for recommendation based on the recommendation level of the image included in the store information without using the preference information. In this case, the generation unit 36 may select an image with the highest recommendation level as the image for recommendation. The generation unit 36 may randomly select an image for recommendation from among images with a recommendation level equal to or higher than a preset recommendation threshold.

In the above embodiment, the image included in the recommendation information is set as the background image of the lock screen of the mobile terminal 10, but it may also be set as the background image of the home screen. In this case, the output unit 37 outputs a command to the mobile terminal 10 to set the image included in the recommendation information as the background image of the home screen of the mobile terminal 10.

The analysis unit 32 may analyze the psychological tendency of each user and generate user information. In this case, the setting unit 33 outputs the user information generated by the analysis unit 32 to the user information storage unit 42 and stores it in the user information storage unit 42. Therefore, the information providing device 30 may not have a group information storage unit 41. The information providing device 30 may not further have the analysis unit 32, the setting unit 33, and the user information storage unit 42. In this case, the information providing device 30 may acquire user information from an external user information storage unit. Similarly, the information providing device 30 may not have a store information storage unit 43. In this case, the information providing device 30 may acquire store information from an external store information storage unit. Similarly, the information providing device 30 may not have a visitor information storage unit 44. In this case, the information providing device 30 may acquire visitor information from an external visitor information storage unit.

The information providing device 30 may not have the extraction unit 34. In this case, the information providing device 30 may acquire all store information from the store information storage unit 43 or an external store information storage unit.

The block diagrams used to explain the above embodiments show functional blocks. These functional blocks (components) are realized by any combination of at least one of hardware and software. There are no particular limitations on the method of realizing each functional block. That is, each functional block may be realized using one device that is physically or logically coupled, or may be realized using two or more devices that are physically or logically separated and directly or indirectly connected (e.g., using wires, wirelessly, etc.). The functional blocks may be realized by combining the one device or the multiple devices with software.

Functions include, but are not limited to, judgement, determination, judgment, calculation, computation, processing, derivation, investigation, search, confirmation, reception, transmission, output, access, resolution, selection, selection, establishment, comparison, assumption, expectation, regard, broadcasting, notifying, communicating, forwarding, configuring, reconfiguring, allocating, mapping, and assignment. For example, a functional block (component) that performs the transmission function is called a transmitting unit or transmitter. As mentioned above, there are no particular limitations on the method of realization for either of these functions.

For example, the information providing device 30 in one embodiment of the present disclosure may function as a computer that performs the processing of the present disclosure. FIG. 12 is a diagram showing an example of the hardware configuration of an information providing device according to one embodiment of the present disclosure. The information providing device 30 described above may be physically configured as a computer device including a processor 1001, a memory 1002, a storage 1003, a communication device 1004, an input device 1005, an output device 1006, and a bus 1007.

In the following description, the term "apparatus" may be interpreted as a circuit, device, unit, etc. The hardware configuration of the information providing apparatus 30 may be configured to include one or more of the devices shown in the figure, or may be configured to exclude some of the devices.

The functions of the information providing device 30 are realized by loading specific software (programs) onto hardware such as the processor 1001 and memory 1002, causing the processor 1001 to perform calculations, control communications via the communication device 1004, and control at least one of the reading and writing of data in the memory 1002 and storage 1003.

The processor 1001, for example, operates an operating system to control the entire computer. The processor 1001 may be configured with a central processing unit (CPU) including an interface with peripheral devices, a control device, an arithmetic unit, and a register. For example, each function of the information providing device 30 described above may be realized by the processor 1001.

The processor 1001 reads out programs (program codes), software modules, data, etc. from at least one of the storage 1003 and the communication device 1004 into the memory 1002, and executes various processes according to the programs. The programs used are those that cause a computer to execute at least some of the operations described in the above-mentioned embodiments. For example, each function of the information providing device 30 may be realized by a control program stored in the memory 1002 and running on the processor 1001. Although the above-mentioned various processes have been described as being executed by one processor 1001, they may be executed simultaneously or sequentially by two or more processors 1001. The processor 1001 may be implemented by one or more chips. The programs may be transmitted from a network via a telecommunications line.

The memory 1002 is a computer-readable recording medium, and may be composed of at least one of, for example, a read only memory (ROM), an erasable programmable ROM (EPROM), an electrically erasable programmable ROM (EEPROM), and a random access memory (RAM). The memory 1002 may also be called a register, a cache, or a main memory (primary storage device). The memory 1002 can store executable programs (program codes), software modules, and the like, for implementing an information providing method according to one embodiment of the present disclosure.

Storage 1003 is a computer-readable recording medium, and may be, for example, at least one of an optical disk such as a CD-ROM (Compact Disc ROM), a hard disk drive, a flexible disk, a magneto-optical disk (e.g., a compact disk, a digital versatile disk, a Blu-ray (registered trademark) disk), a smart card, a flash memory (e.g., a card, a stick, a key drive), a floppy (registered trademark) disk, and a magnetic strip. Storage 1003 may also be referred to as an auxiliary storage device. The above-mentioned storage medium may be, for example, a database, a server, or other suitable medium including at least one of memory 1002 and storage 1003.

The communication device 1004 is hardware (transmission/reception device) for communicating between computers via at least one of a wired network and a wireless network, and is also called, for example, a network device, a network controller, a network card, or a communication module. The communication device 1004 may be configured to include a high-frequency switch, a duplexer, a filter, a frequency synthesizer, etc., to realize at least one of Frequency Division Duplex (FDD) and Time Division Duplex (TDD). For example, the above-mentioned acquisition unit 31 and output unit 37, etc. may be realized by the communication device 1004.

The input device 1005 is an input device (e.g., a keyboard, a mouse, a microphone, a switch, a button, a sensor, etc.) that accepts input from the outside. The output device 1006 is an output device (e.g., a display, a speaker, an LED lamp, etc.) that performs output to the outside. Note that the input device 1005 and the output device 1006 may be integrated into one device (e.g., a touch panel).

Each device, such as the processor 1001 and the memory 1002, is connected by a bus 1007 for communicating information. The bus 1007 may be configured using a single bus, or may be configured using different buses between each device.

The information providing device 30 may be configured to include hardware such as a microprocessor, a digital signal processor (DSP), an application specific integrated circuit (ASIC), a programmable logic device (PLD), or a field programmable gate array (FPGA), and some or all of the functional blocks may be realized by the hardware. For example, the processor 1001 may be implemented using at least one of these pieces of hardware.

Notification of information is not limited to the aspects/embodiments described in this disclosure and may be performed using other methods.

The processing steps, sequences, flow charts, etc. of each aspect/embodiment described in this disclosure may be reordered unless inconsistent. For example, the methods described in this disclosure present elements of various steps using an example order and are not limited to the particular order presented.

Information, etc. may be output from a higher layer to a lower layer, or from a lower layer to a higher layer. Information, etc. may be input/output via multiple network nodes.

The input and output information may be stored in a specific location (e.g., memory) or may be managed using a management table. The input and output information may be overwritten, updated, or added to. The output information may be deleted. The input information may be transmitted to another device.

The determination may be based on a value represented by one bit (0 or 1), a Boolean (true or false) value, or a comparison of numerical values (e.g., with a predetermined value).

Each aspect/embodiment described in this disclosure may be used alone, in combination, or switched according to execution. Notification of specific information (e.g., notification that "X is the case") is not limited to explicit notification, but may be performed implicitly (e.g., by not notifying the specific information).

Although the present disclosure has been described in detail above, it is clear to those skilled in the art that the present disclosure is not limited to the embodiments described herein. The present disclosure can be implemented in modified and altered forms without departing from the spirit and scope of the present disclosure as defined by the claims. Therefore, the description of the present disclosure is intended as an illustrative example and does not have any limiting meaning on the present disclosure.

Software shall be construed broadly to mean instructions, instruction sets, code, code segments, program code, programs, subprograms, software modules, applications, software applications, software packages, routines, subroutines, objects, executable files, threads of execution, procedures, functions, etc., whether referred to as software, firmware, middleware, microcode, hardware description language, or otherwise.

Software, instructions, information, and the like may be transmitted and received via a transmission medium. For example, if the software is transmitted from a website, server, or other remote source using wired technologies (such as coaxial cable, fiber optic cable, twisted pair, Digital Subscriber Line (DSL)), and/or wireless technologies (such as infrared, microwave), these wired and/or wireless technologies are included within the definition of a transmission medium.

The information, signals, etc. described in this disclosure may be represented using any of a variety of different technologies. For example, the data, instructions, commands, information, signals, bits, symbols, chips, etc. that may be referred to throughout the above description may be represented by voltages, currents, electromagnetic waves, magnetic fields or magnetic particles, optical fields or photons, or any combination thereof.

In addition, the terms explained in this disclosure and the terms necessary for understanding this disclosure may be replaced with terms having the same or similar meanings.

As used in this disclosure, the terms "system" and "network" are used interchangeably.

The information, parameters, etc. described in this disclosure may be expressed using absolute values, may be expressed using relative values from a predetermined value, or may be expressed using other corresponding information.

The names used for the parameters described above are not limiting in any way. Furthermore, the formulas etc. using these parameters may differ from the formulas etc. explicitly disclosed in this disclosure.

The terms "determining" and "determining" as used in this disclosure may encompass a wide variety of actions. "Determining" and "determining" may include, for example, judging, calculating, computing, processing, deriving, investigating, looking up, searching, inquiring (e.g., searching in a table, database, or other data structure), ascertaining, and the like. "Determining" and "determining" may include receiving (e.g., receiving information), transmitting (e.g., sending information), input, output, accessing (e.g., accessing data in a memory), and the like. "Determining" and "determining" may include resolving, selecting, choosing, establishing, comparing, and the like. In other words, "judgment" and "decision" can include regarding some action as having been "judged" or "decided." "Judgment (decision)" may also be interpreted as "assuming," "expecting," or "considering," etc.

The terms "connected" and "coupled", or any variation thereof, refer to any direct or indirect connection or coupling between two or more elements, and may include the presence of one or more intermediate elements between two elements that are "connected" or "coupled" to each other. The coupling or connection between elements may be physical, logical, or a combination thereof. For example, "connected" may be read as "access". As used in this disclosure, two elements may be considered to be "connected" or "coupled" to each other using at least one of one or more wires, cables, and printed electrical connections, as well as electromagnetic energy having wavelengths in the radio frequency range, microwave range, and optical (both visible and invisible) range, as some non-limiting and non-exhaustive examples.

As used in this disclosure, the phrase "based on" does not mean "based only on," unless expressly stated otherwise. In other words, the phrase "based on" means both "based only on" and "based at least on."

Any reference to elements using designations such as "first" and "second" used in this disclosure does not generally limit the quantity or order of those elements. These designations may be used in this disclosure as a convenient way to distinguish between two or more elements. Thus, a reference to a first and a second element does not imply either that only two elements may be employed or that the first element must precede the second element in any way.

The "part" in the configuration of each of the above devices may be replaced with a "circuit" or a "device", etc.

When the terms "include," "including," and variations thereof are used in this disclosure, these terms are intended to be inclusive, similar to the term "comprising." Additionally, the term "or," as used in this disclosure, is not intended to be an exclusive or.

In this disclosure, where articles have been added by translation, such as "a," "an," and "the" in English, this disclosure may include that the nouns following these articles are plural.

In this disclosure, the term "A and B are different" may mean "A and B are different from each other." The term may also mean "A and B are each different from C." Terms such as "separate" and "combined" may also be interpreted in the same way as "different."

1...information provision system, 10...mobile terminal (terminal device), 20...store terminal, 30...information provision device, 31...acquisition unit, 32...analysis unit, 33...setting unit, 34...extraction unit, 35...determination unit, 36...generation unit, 37...output unit, 41...group information storage unit, 42...user information storage unit, 43...store information storage unit, 44...visitor information storage unit, 1001...processor, 1002...memory, 1003...storage, 1004...communication device, 1005...input device, 1006...output device, 1007...bus.

Claims (4)

A determination unit that selects, based on user information including user characteristics, recommendation candidates associated with the candidate information that satisfies a condition acceptable to the user from among candidate information that is information about recommendation candidates, and determines a recommendation target from among the recommendation candidates;
A generation unit that generates recommendation information related to the recommendation target;
an output unit that outputs the recommendation information to a terminal device of the user;
Equipped with
The candidate information includes a congestion degree of the recommendation candidate,
The acceptable condition includes an acceptable congestion level that is an acceptable congestion level for the user,
The determination unit selects the recommendation candidate associated with the candidate information including the congestion degree less than the allowable congestion degree ,
the generation unit selects an image for recommendation from images included in the candidate information based on a similarity between a vector based on a bias value of a psychological tendency included in behavioral information of a user who acted according to the recommendation information and a vector based on a bias value of a psychological tendency included in the user information of the user to whom the recommendation information is to be provided, and generates the recommendation information including the image;
Information providing device. The candidate information includes a candidate cost that is a cost related to an object provided by the recommendation candidate,
The acceptable conditions include an acceptable cost, which is a cost that the user is willing to pay;
The information providing device according to claim 1 , wherein the determination unit selects the recommendation candidate associated with the candidate information including the candidate cost that is less than the allowable cost. An acquisition unit that acquires user location information that is location information of the user,
The candidate information includes candidate location information that is location information of the recommendation candidate,
the acceptable conditions include an acceptable distance that is an acceptable distance for the user to travel;
The information providing device according to claim 1 or claim 2, wherein the determination unit selects the recommendation candidate such that a candidate distance, which is a distance from the user to the recommendation candidate calculated based on the user position information and the candidate position information, is less than the allowable distance. The information providing device according to any one of claims 1 to 3, wherein the generating unit selects an image for recommendation from images included in the candidate information based on at least one of preference information included in the user information and the candidate information, and generates the recommendation information including the image.

Images