JP7747535B2 - Information providing device, information providing method, and information providing program - Google Patents

Description

The present invention relates to an information provision device, an information provision method, and an information provision program.

In recent years, with the rapid spread of the Internet, analytical techniques using various types of information on the Internet have been developed. For example, techniques are known for analyzing customers (or "users" as appropriate) by taking into account trends over time using purchase history data, etc.

Japanese Patent Application Laid-Open No. 2015-146145

However, the above-mentioned conventional technologies leave room for improvement in terms of providing useful information to users.

This application has been made in light of the above, and aims to provide an information provision device, an information provision method, and an information provision program that enable useful information to be provided to users.

To solve the above-mentioned problems and achieve the objectives, the system is characterized by comprising a collection unit that collects user information including the user's behavioral history, a calculation unit that calculates the user's level of understanding, which indicates the depth of the user's understanding, for each specified region based on the user information, and a generation unit that generates information to be provided to the user based on the level of understanding.

The information provision method according to the present invention is executed by an information provision device and is characterized by including: a collection step of collecting user information including the user's behavioral history; a calculation step of calculating, for each predetermined region based on the user information, a level of understanding that indicates the depth of the user's understanding; and a generation step of generating information to be provided to the user based on the level of understanding.

The information provision program of the present invention is characterized by having a computer execute a collection step of collecting user information including the user's behavioral history, a calculation step of calculating the user's level of understanding, which indicates the depth of the user's understanding, for each specified region based on the user information, and a generation step of generating information to be provided to the user based on the level of understanding.

This invention can provide useful information to users.

FIG. 1 is a diagram illustrating an example of processing of an information providing system according to an embodiment. FIG. 2 is a block diagram illustrating an example of the configuration of the information providing device according to the embodiment. FIG. 3 is a diagram illustrating an example of a user information storage unit according to the embodiment. FIG. 4 is a diagram illustrating an example of a region information storage unit according to the embodiment. FIG. 5 is a diagram illustrating an example of an understanding level information storage unit according to the embodiment. FIG. 6 is a diagram illustrating an example of an understanding level information storage unit according to the embodiment. FIG. 7 is a diagram illustrating an example of a provided information storage unit according to the embodiment. FIG. 8 is a flowchart illustrating an example of the flow of the information providing process according to the embodiment. FIG. 9 illustrates an example of a hardware configuration.

Below, detailed explanations will be given of the information provision device, information provision method, and information provision program (hereinafter, "embodiments") according to the present application, with reference to the drawings. Note that the information provision device, information provision method, and information provision program according to the present application are not limited to these embodiments. Furthermore, identical components in the following embodiments will be designated by the same reference numerals, and duplicate explanations will be omitted.

[Embodiment]
The following describes the processing of the information providing system 100 according to the embodiment, the configuration of the information providing device 10, a specific example of the information providing processing, and the flow of the information providing processing, and finally, the effects of this embodiment.

1. Processing of the information provision system 100
The processing of an information provision system (referred to as "this system" as appropriate) 100 according to this embodiment will be described using Fig. 1. Fig. 1 is a diagram showing an example of the processing of the information provision system according to this embodiment. Below, an example configuration of this system 100, the processing of this system 100, and the effects of this system 100 will be described in that order.

(1-1. Example of the configuration of the system 100)
1 includes an information providing device 10, a user terminal 20, and a local information database 30. The information providing device 10, the user terminal 20, and the local information database 30 are communicably connected via a predetermined communication network (not shown) by wire or wirelessly. Note that the system 100 may include a plurality of information providing devices 10, a plurality of user terminals 20, and a plurality of local information databases 30.

(1-1-1. Information providing device 10)
The information providing device 10 is a device that transmits and receives data between the user terminal 20 and the local information database 30, and is realized, for example, by a server device or a cloud system. The example in Fig. 1 shows a case where the information providing device 10 is realized by a server device.

(1-1-2. User terminal 20)
The user terminal 20 is a device (computer) used by a user U to browse web pages, conduct internet shopping on the web, etc. The user terminal 20 accepts operations by the user U. The user terminal 20 may be realized, for example, by a smartphone, a tablet terminal, a notebook PC (Personal Computer), a desktop PC, a mobile phone, a PDA (Personal Digital Assistant), etc. The example in FIG. 1 shows a case where the user terminal 20 is realized by a smartphone.

Furthermore, in the following explanation, user U may be used to represent multiple users. Furthermore, among users U, a "high comprehension user" with a high level of comprehension, as described below, may be referred to as user UA, and a "low comprehension user" with a low level of comprehension may be referred to as user UB.

(1-2. Processing of the System 100)
(1-2-1. Processing of step S1)
In the present system 100, first, the information providing device 10 collects user information of the user U from the user terminal 20 (step S1). Here, the user information refers to information about the user U of the present system 100, including, for example, user attributes, behavioral history, etc. The user attributes include the user U's gender, age, generation, occupation, annual income, place of residence, marital status, whether or not they have children, and categories of interest. The behavioral history includes location information regarding the user U's location and movements acquired by the user terminal 20, as well as the user U's website search history, browsing history, and online shopping purchase history. Furthermore, the user information may include, but is not limited to, screen information of the user terminal 20, biometric information of the user U, etc. In the example of FIG. 1 , the information providing device 10 acquires the user information of the user U from the user terminal 20. However, the information providing device 10 may also acquire the user information from the user U's terminal, other terminals, databases, etc. (not shown).

(1-2-2. Processing of step S2)
Second, in the present system 100, the information providing device 10 refers to the local information database 30 and acquires local information (step S2). Here, local information refers to information including, but not limited to, map information on place names, topography, etc., and location information on POIs (Points of Interest), which are specific locations within the local area. In the example of FIG. 1, the information providing device 10 acquires the local information from the local information database 30. However, the information providing device 10 may acquire the local information from the user terminal 20 or another terminal, database, etc. (not shown).

(1-2-3. Processing of step S3)
Third, in the present system 100, the information providing device 10 calculates the user U's level of understanding for each region from the collected user information and region information (step S3). For example, the information providing device 10 uses a machine learning model to calculate the level of understanding for each region based on the user U's stay locations, travel routes, stay times, and travel speed indicated by the user U's behavioral history included in the user information, and the location information included in the region information, so that the level of understanding takes a value between 0 and 1 depending on the user U's depth of understanding of the region. In this case, the information providing device 10 calculates the level of understanding using a machine learning model such as a deep neural network (DNN) that is trained to output the user U's level of understanding for each region when the user information and region information of the user U are input. Alternatively, the information providing device 10 may calculate the level of understanding based on rules.

Step S3 described above will now be explained using a specific example. Below, we will explain the example of user U1, who lives in City X and commutes to a company in City X.

For example, the information providing device 10 calculates a higher level of understanding the more frequently user U1 visits a stay point (e.g., the higher the level of understanding in area X1 where user U1's home is). The information providing device 10 also calculates a higher level of understanding the more frequently user U1 uses a travel route (probe) (e.g., the higher the level of understanding in area X1 where user U1's home is and area X2 where user U1's company is). The information providing device 10 also calculates a higher level of understanding the longer user U1 stays (e.g., the higher the level of understanding in area X3 where user U1 has a coffee shop that he or she frequents every weekend). The information providing device 10 also calculates a higher level of understanding the slower user U1 moves (e.g., the higher the level of understanding in area X4 where user U1 has a park where he or she takes a walk every weekend). The information providing device 10 also calculates a higher level of understanding the more frequently user U1 visits a specific point indicated by the point information (e.g., the higher the level of understanding in area X5 where user U1 has lunch).

(1-2-4. Processing of step S4)
Fourth, in the present system 100, the information providing device 10 determines the user characteristics and regional characteristics of the user U from the calculated level of understanding of the user U (step S4). For example, the information providing device 10 determines, as the user characteristics of the user U, whether the user U is a user UA who has a deep understanding in a certain region or a certain area, or a user UB who has a shallow understanding. Furthermore, the information providing device 10 determines, as the regional characteristics, the regional characteristics for the user U, such as areas that the user U frequently visits within a certain area or areas that the user U rarely visits. Furthermore, the information providing device 10 can also determine the characteristics of each region within a certain area by comparing the levels of understanding between users.

The above-mentioned step S4 will be explained using a specific example. Below, we will explain the example of user U1, who has lived in City X for many years, and user U2, who recently moved to City X.

For example, the information providing device 10 determines that user U1 has a deep level of understanding of City X based on the user U1's level of understanding (e.g., of all areas in City X, more than 80% have an understanding level of 0.5 or higher). On the other hand, the information providing device 10 determines that user U2 has a shallow level of understanding of City X based on the user U2's level of understanding (e.g., of all areas in City X, less than 20% have an understanding level of 0.5 or higher). Furthermore, the information providing device 10 determines the characteristics of areas frequently visited by users U1 and U2 based on the levels of understanding of users U1 and U2 (e.g., area X6, where both users U1 and U2 have an understanding level of 0.5 or higher, has a shopping mall). Furthermore, the information providing device 10 determines the characteristics of the area, such as hidden spots known to user U1, who has lived in city X for many years, based on the difference in understanding between user U1 and user U2 (for example, in area X3, where user U1's understanding level is 0.5 or higher and user U2's understanding level is less than 0.5, there is a coffee shop that user U1 visits every weekend).

The information providing device 10 can also reflect the characteristics of the area determined in step S4 in the location information and send it to the area information database 30 (e.g., stores frequented by locals, stores frequented by tourists).

(1-2-5. Processing of step S5)
Fifth, in the present system 100, the information providing device 10 generates information to be provided to the user U based on the calculated level of understanding of the user U and the determined user characteristics of the user U (step S5). Here, the provided information is information about a region or location generated based on the location information, and is information about tourist spots such as famous places and sites that the user U has not visited, commercial facilities such as restaurants, public facilities such as parks and libraries, etc., but is not particularly limited thereto.

The above-mentioned step S5 will be explained using a specific example. Below, we will explain the examples of user U1, who has lived in city X for many years, user U2, who recently moved to city X, and user U3, who has lived in city Y for many years and is visiting city X on a business trip.

For example, the information providing device 10 generates information to be provided to user U1, who has a deep understanding of X City, recommending the visit of unvisited locations (e.g., information on unvisited restaurants in area X5, where the level of understanding is high, and information on unvisited parks in area X7, where the level of understanding is low). Furthermore, the information providing device 10 generates information to be provided to user U2, who has a shallow understanding of X City, recommending the visit of locations frequently visited by user U1, who has a deep understanding of X City (e.g., information on a coffee shop that user U1 frequents every weekend). Furthermore, the information providing device 10 generates information to be provided to user U3, who has a shallow understanding of X City but a deep understanding of Y City, recommending the visit of locations in Y City similar to the locations frequently visited by user U3 (e.g., information on a park in area X4 in X City that is similar to a park in area Y1 in Y City where user U3 often takes walks).

(1-2-6. Processing of step S6)
Sixth, in the present system 100, the information providing device 10 transmits the generated information to the user terminal 20 of user U (step S6). Using the example of step S5 above, the information providing device 10 transmits information to be provided that recommends visiting unvisited locations to user U1 who has a deep understanding of city X. The information providing device 10 also transmits information to be provided that recommends visiting locations frequently visited by user U1 who has a deep understanding of city X to user U2 who has a shallow understanding of city X. The information providing device 10 also transmits information to be provided that recommends visiting locations in city Y similar to locations frequently visited by user U3 to user U3 who has a shallow understanding of city X but a deep understanding of city Y.

(1-2-7. Processing of step S7)
Seventh, in the present system 100, the information providing device 10 learns the calculated level of understanding (step S7). For example, when information indicating that the user U actually has a deep understanding of a specific region is input to the machine learning model, the information providing device 10 learns using backpropagation or the like so as to output the user U's level of understanding of the region as a numerical value approaching "1." Furthermore, when information indicating that the user U actually has a shallow understanding of a specific region is input to the machine learning model, the information providing device 10 learns using backpropagation or the like so as to output the user U's level of understanding of the region as a numerical value approaching "0."

(1-3. Effects of the system 100)
In the present system 100, the information providing device 10 collects user information including the behavioral history of the user U, calculates a level of understanding indicating the depth of understanding of the user U for each predetermined region based on the collected user information, and generates information to be provided to the user U based on the calculated level of understanding. The information providing device 10 also collects area information including location information about predetermined locations included in the region, and calculates a level of understanding for each region based on the user U's stay locations, travel routes, stay times, and travel speed indicated in the user U's behavioral history, or the collected location information. Based on the user U's level of understanding for each region included in the predetermined range, the information providing device 10 determines whether the user UA has a deep understanding of the region or whether the user UA does not have a deep understanding of the region. Based on the user U's level of understanding for each region included in the predetermined range, the information providing device 10 can also determine the characteristics of each region based on the difference in the level of understanding of the users U between the regions, or the difference in the level of understanding of the users U between multiple users U for each region. In other words, the system 100 can not only determine which areas the user U is familiar with and which areas he or she is not familiar with for each area in which he or she is active, but can also determine the user characteristics, such as whether or not the user U is familiar with the entire area in which he or she is active, and the area characteristics, which indicate the characteristics of the points included in the area.

In addition, the system 100 provides a user UA with a deep understanding with information about areas and locations that the user UA has little understanding of. As a result, the system 100 allows local residents to obtain information about spots that they have never visited before, which is expected to lead to rediscovery of their local area. In addition, the system 100 provides a user UB with a shallow understanding with information about areas and locations that a user UA with a deep understanding of is known to. As a result, the system 100 allows tourists who are unfamiliar with the area to obtain information about spots that only locals know about, which is expected to lead to more effective sightseeing.

Furthermore, in addition to providing information to user U, system 100 can also acquire various useful information. For example, system 100 can acquire information related to targeting. Specifically, system 100 can determine the characteristics of an area or location, which can be used to effectively find users U to conduct interviews and surveys with. System 100 can also acquire information related to "expert" areas (hidden gems). Specifically, system 100 can identify "expert" stores in an area from the destinations visited by users U who have a deep understanding of the area. System 100 can also acquire information related to the evaluation of areas that do not have POI information. Specifically, system 100 can add information such as comfortable walking and running courses to a map.

2. Configuration of information providing device 10
The configuration of the information providing device 10 according to the embodiment will be described with reference to Fig. 2. Fig. 2 is a block diagram showing an example configuration of the information providing device 10 according to the embodiment. As shown in Fig. 2, the information providing device 10 includes a communication unit 11, a storage unit 12, and a control unit 13. The information providing device 10 may also include an input unit (e.g., a keyboard, a mouse, etc.) that accepts various operations from an administrator of the information providing device 10, and a display unit (e.g., a liquid crystal display, etc.) that displays various information.

(2-1. Communication unit 11)
The communication unit 11 is realized by, for example, a network interface card (NIC), etc. The communication unit 11 is connected to a predetermined communication network via a wired or wireless connection, and transmits and receives information to and from various devices.

(2-2. Storage unit 12)
The storage unit 12 is realized by, for example, a semiconductor memory element such as a random access memory (RAM) or a flash memory, or a storage device such as a hard disk or an optical disk. As shown in Fig. 2, the storage unit 12 according to the embodiment includes a user information storage unit 12a, a region information storage unit 12b, an understanding level information storage unit 12c, and a provided information storage unit 12d. The storage unit 12 stores various types of information referenced by the control unit 13 when it operates and various types of information acquired when the control unit 13 operates.

(2-2-1. User information storage unit 12a)
The user information storage unit 12a stores various types of information (user information) related to the user U. An example of information stored in the user information storage unit 12a will now be described with reference to FIG. 3. FIG. 3 is a diagram illustrating an example of the user information storage unit 12a according to the embodiment. In the example of FIG. 3, the user information storage unit 12a has items such as "user ID,""userattributes," and "behavioral history."

"User ID" refers to identification information used to identify user U. "User attributes" refers to information such as user U's gender, age, generation, occupation, annual income, place of residence, marital status, whether they have children, and categories of interest. "Behavioral history" refers to information including location information regarding user U's location, movements, etc. acquired by user terminal 20, as well as user U's website search history, browsing history, and internet shopping purchase history.

That is, Figure 3 shows an example in which user U, identified by user ID "UID#1," has user attribute "user attribute #1" and behavior history "behavior history #1."

(2-2-2. Regional information storage unit 12b)
The region information storage unit 12b stores various information (region information) related to regions (areas). An example of information stored in the region information storage unit 12b will be described below with reference to FIG. 4. FIG. 4 is a diagram illustrating an example of the region information storage unit 12b according to the embodiment. In the example of FIG. 4, the region information storage unit 12b has fields such as "region ID" and "region attribute."

"Area ID" indicates identification information for identifying areas divided into specified units. "Area attributes" indicate information that contributes to the classification of areas, such as map information related to place names and topography, and location information related to POIs, which are specific points within the area.

That is, Figure 4 shows an example in which the region attribute for the region identified by the region ID "AID#1" is "region attribute #1."

(2-2-3. Comprehension level information storage unit 12c)
The understanding information storage unit 12c stores the understanding level (understanding level information) for each region calculated by the calculation unit 13b of the control unit 13. An example of information stored in the understanding level information storage unit 12c will be described below with reference to FIGS. 5 and 6. FIGS. 5 and 6 are diagrams showing an example of the understanding level information storage unit 12c according to the embodiment. In the example of FIG. 5, the understanding level information storage unit 12c has fields such as "user ID,""regionID," and "understanding level."

"User ID" indicates identification information for identifying user U. "Area ID" indicates identification information for identifying areas divided into specified units. "Level of understanding" is a numerical value indicating the depth of user U's understanding for each area.

That is, Figure 5 shows an example in which, for user U identified by user ID "UID#1", the level of understanding for the area identified by area ID "AID#1" is "Level of Understanding #1", and the level of understanding for the area identified by area ID "AID#2" is "Level of Understanding #2".

Furthermore, as shown in the example of Figure 6, the understanding information storage unit 12c can also store understanding information expressed using a square frame indicating the area, which is the unit for calculating the user U's understanding, and shading indicating the user U's understanding in that area. In this case, the understanding information storage unit 12c may store understanding information expressed using a frame and shading superimposed on an actual map.

For example, "A1" to "A5," "B1" to "B5," "C1" to "C5," "D1" to "D5," and "E1" to "E5" in Figure 6 are symbols used to identify the regions for which comprehension is calculated. Here, the above regions are a predetermined area (e.g., prefecture, city, town, or village) divided into predetermined units (e.g., 100m square), but there are no particular restrictions on the size, division, or type of the area that includes the region, or the size and shape of the division into regions.

In other words, in Figure 6, the divided frames have medium shading for "B1" to "B4," "C5," and "D5," indicating that the level of understanding of user U in the corresponding area is medium. Similarly, the frames have dark shading for "C2" to "C4," "D3" to "D4," and "E4" to "E5," indicating that the level of understanding of user U in the corresponding area is high. Furthermore, frames other than those mentioned above do not have shading, indicating that the level of understanding of user U in the corresponding area is low.

(2-2-4. Provided information storage unit 12d)
The provided information storage unit 12d stores the provided information generated by the generation unit 13d of the control unit 13. An example of information stored in the provided information storage unit 12d will now be described with reference to FIG. 7. FIG. 7 is a diagram illustrating an example of the provided information storage unit 12d according to the embodiment. In the example of FIG. 7, the provided information storage unit 12d has items such as "user ID,""areaID," and "provided information."

"User ID" indicates identification information for identifying user U. "Area ID" indicates identification information for identifying an area divided into predetermined units. "Provided information" is information including advertisements, content, etc. related to recommended locations selected for each area of user U.

That is, Figure 7 shows an example in which, for user U identified by user ID "UID#1," the provided information for the area identified by area ID "AID#1" is "Provided Information #1," and the provided information for the area identified by area ID "AID#2" is "Provided Information #2."

(2-3. Control unit 13)
The control unit 13 is realized by, for example, a central processing unit (CPU) or a micro processing unit (MPU) executing various programs (corresponding to an example of an information processing program) stored in a storage device inside the information providing device 10 using RAM as a work area. The control unit 13 is also realized by, for example, an integrated circuit such as an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA).

As shown in FIG. 2, the control unit 13 has a collection unit 13a, a calculation unit 13b, a determination unit 13c, a generation unit 13d, a transmission unit 13e, and a learning unit 13f, and realizes or executes the information processing functions and actions described below. Note that the internal configuration of the control unit 13 is not limited to the configuration shown in FIG. 2, and may be other configurations as long as they perform the information processing described below. Furthermore, the connection relationships between the processing units in the control unit 13 are not limited to the connection relationships shown in FIG. 2, and may be other connection relationships.

(2-3-1. Collection unit 13a)
The collection unit 13a collects user information including the behavioral history of the user U. For example, the collection unit 13a collects, as user information, from the user terminal 20, the user U's stay points, movement routes, stay times, and movement speeds indicated by the user U's behavioral history. The collection unit 13a also collects area information including location information related to specific locations included in a specific area. For example, the collection unit 13a collects, as area information, map information related to place names, topography, etc., and area attributes including location information related to POIs, which are specific points included in the area, from the area information database 30. The collection unit 13a stores the collected user information in the user information storage unit 12a. The collection unit 13a also stores the collected area information in the area information storage unit 12b.

(2-3-2. Calculation unit 13b)
The calculation unit 13b calculates the level of understanding of the user U, which indicates the depth of understanding, for each predetermined region based on the user information. Furthermore, the calculation unit 13b calculates the level of understanding of the user U for each predetermined region based on the region information. For example, the calculation unit 13b calculates the level of understanding of the user U for each predetermined region based on the user U's stay location, travel route, stay time, travel speed, or location information indicated in the user U's behavior history. Furthermore, the calculation unit 13b can also calculate the level of understanding of the user U for each predetermined day of the week or time period. For example, the calculation unit 13b can calculate the level of understanding of the user U for each predetermined region, divided into weekdays and holidays, or divided into daytime and evening time periods.

To explain the calculation method, the calculation unit 13b calculates the level of understanding of user U using a machine learning model such as DNN that has been trained to output the level of understanding of user U for each specified region when user information and region information of user U are input. The information providing device 10 may also calculate the level of understanding of user U on a rule-based basis.

Regarding the calculated numerical values, the calculation unit 13b calculates the level of understanding of user U so that it takes a numerical value between 0 and 1 depending on the depth of understanding for each specified region. The calculation unit 13b may also calculate the level of understanding of user U so that it takes a numerical value between 0 and 100% depending on the depth of understanding for each specified region, and the range and unit of the calculated numerical values are not particularly limited.

Explaining the information used for the calculation, the calculation unit 13b calculates the number of visits to stay points by user U, and calculates a higher level of understanding for user U in areas that include stay points that are visited more frequently. The calculation unit 13b also calculates the number of times a travel route is used by user U, and calculates a higher level of understanding for areas that include a travel route that is used more frequently. The calculation unit 13b also calculates the stay time of user U in each specified area, and calculates a higher level of understanding for areas where the stay time is longer. The calculation unit 13b also calculates the travel speed for each specified area, and calculates a higher level of understanding for areas where the travel speed is slower. The calculation unit 13b also calculates the visit rate to specific points for each specified area indicated by the location information, and calculates a higher level of understanding for areas where the visit rate is higher.

The calculation unit 13b acquires user information from the user information storage unit 12a. The calculation unit 13b also acquires regional information, including location information, from the regional information storage unit 12b. Meanwhile, the calculation unit 13b stores the calculated level of understanding of user U in the level of understanding information storage unit 12c.

(2-3-3. Determination unit 13c)
The determination unit 13c determines whether a user UA has a deep understanding of the area (high-level user) or a user UB does not have a deep understanding of the area (low-level user) based on the user U's level of understanding for each area included in the area. For example, if the predetermined area is the entire city X, and the predetermined areas are divided into 100 areas, and the area where user U's level of understanding satisfies a predetermined value of 0.5 or more is 80% or more of the areas, the determination unit 13c determines that user U is a high-level user UA with a deep understanding of city X. Similarly, if the area where user U's level of understanding satisfies a predetermined value of 0.5 or more is less than 20% of the areas, the determination unit 13c determines that user U is a low-level user UB with a poor understanding of city X.

The determination unit 13c also determines the characteristics of each specified region based on the difference in the level of understanding of the user U between specified regions. For example, the determination unit 13c can use the difference in level of understanding between regions, or the difference in level of understanding between days of the week or time periods, to determine living areas that include "home," "work," etc., or areas that include favorite spots such as "cafes" and "parks."

The determination unit 13c also determines the characteristics of each specified region based on the difference in the level of understanding of users U for each specified region among multiple users U. For example, the determination unit 13c can use the difference in the level of understanding between high-level user UA and low-level user UB to determine a region that includes hidden spots such as restaurants for "experts" that are recommended to low-level user UB. The determination unit 13c can also use the difference in the level of understanding between high-level user UA and low-level user UB, who share common user attributes (e.g., walking as a hobby, liking to eat out), to determine a region that includes spots that take into account categories of interest that are recommended to low-level user UB.

The determination unit 13c acquires the user U's level of understanding from the understanding level information storage unit 12c. Meanwhile, the determination unit 13c may store the determination result in the understanding level information storage unit 12c. Furthermore, the determination unit 13c may transmit the determination result to the regional information database 30 or a business operator terminal (not shown).

(2-3-4. Generation unit 13d)
The generation unit 13d generates information to be provided to the user U based on the level of understanding of the user U. For example, when the user U is a high-understanding user UA, the generation unit 13d generates information to be provided to the user U using location information related to locations included in an area within a predetermined range where the user U's level of understanding is below a predetermined threshold. To explain using a specific example, when the user U is a high-understanding user UA in city X, the generation unit 13d generates information to be provided to the user U using information about restaurants included in an area within city X where the user U resides where the user U's level of understanding is below 0.5.

Furthermore, if user U is a low-understanding user UB, the generation unit 13d generates information to be provided to user U using location information related to locations included in a specified area where the understanding level of high-understanding user UA within a specified range is equal to or higher than a specified threshold. Explaining this using a specific example, if user U is a low-understanding user UB in city X, the generation unit 13d generates information to be provided to user U using information about restaurants included in an area in city X where user U visited for sightseeing and where the understanding level of high-understanding user UA is 0.5 or higher.

The generation unit 13d acquires the understanding level information from the understanding level information storage unit 12c. The generation unit 13d also stores the generated provision information in the provision information storage unit 12d.

(2-3-5. Transmitting unit 13e)
The transmitting unit 13e transmits the provided information generated by the generating unit 13d to the user U. To explain using a specific example, the transmitting unit 13e transmits to the user U, who is a high-understanding user UA in the city X, provided information about restaurants located in an area in the city X where the user U's understanding level is less than 0.5. Furthermore, the transmitting unit 13e transmits to the user U, who is a low-understanding user UB in the city X that the user U visited for sightseeing, provided information about restaurants located in an area in the city X where the high-understanding user UA's understanding level is 0.5 or higher.

The transmission unit 13e acquires the provided information from the provided information storage unit 12d. The transmission unit 13e may also transmit the provided information to a business operator terminal or database (not shown).

(2-3-6. Learning unit 13f)
When the user information and regional information of the user U are input, the learning unit 13f learns the machine learning model so as to output the understanding level of the user U for each predetermined region. At this time, the learning unit 13f may learn the machine learning model by backpropagation or the like.

For example, when information indicating that user U has frequently visited points of stay within the area is input to the machine learning model, the learning unit 13f trains the machine learning model so that it outputs user U's level of understanding as a number approaching "1."

In addition, when information about a travel route that user U frequently uses within the area is input to the machine learning model, the learning unit 13f trains the machine learning model so that it outputs user U's level of understanding as a number approaching "1."

In addition, the learning unit 13f trains the machine learning model so that when information indicating that user U has spent a long time in the area is input to the machine learning model, the learning unit 13f outputs user U's level of understanding as a number approaching "1."

In addition, the learning unit 13f trains the machine learning model so that when information that user U's movement speed in the area is slow is input to the machine learning model, the learning unit 13f outputs user U's level of understanding as a number approaching "1."

In addition, the learning unit 13f trains the machine learning model so that when information indicating that user U has a high rate of visits to specific locations within the area is input to the machine learning model, the learning unit 13f outputs user U's level of understanding as a number approaching "1."

[3. Specific examples of information provision processing]
(3-1. Specific Example 1)
A first specific example of the information provision process according to the embodiment will be described below. First, the information providing device 10 acquires information on specific points of interest (POIs) within City X based on area information. Next, the information providing device 10 acquires information on POIs within City X visited by user U based on the behavioral history, calculates the visit rate to POIs for each area within City X, and calculates user U's level of understanding for each area. If a predetermined percentage (e.g., 80%) or more of the areas within City X have a high level of understanding (e.g., 0.5 or higher), the information providing device 10 determines that user U has a deep understanding of City X. As described above, by using behavioral history such as location information, the information providing device 10 can discover users who do not normally provide information but who are familiar with local "familiar" places. In other words, the information providing device 10 can identify user U's characteristics.

(3-2. Specific Example 2)
A second specific example of the information provision process according to the embodiment will be described below. First, the information provision device 10 performs a process for calculating a level of understanding using the time spent at restaurants to identify a user UA who has a deep understanding of City X and a user UB who has a shallow understanding of City X. Furthermore, the information provision device 10 uses the search history indicated by the behavioral history to identify that users UA and UB share an interest in eating and drinking. Then, the information provision device 10 compares the level of understanding of user UA with the level of understanding of user UB and calculates the difference between the two to identify an area where restaurants only user UA knows are located. As described above, the information provision device 10 can discover local "expert" places by using the search history and user attributes and comparing the levels of understanding of user UA who has a deep understanding with user UB who has a shallow understanding. In other words, the information provision device 10 can find the characteristics of an area from the information of multiple users U.

(3-3. Specific Example 3)
A third specific example of the information provision process according to the embodiment will be described below. First, the information providing device 10 executes a comprehension level calculation process using the user U's stay time and travel time in City X to calculate the user U's comprehension level for each region. The information providing device 10 also acquires information on specific points of interest (POIs) within City X based on the region information. The information providing device 10 then compares the user U's comprehension levels at each region and calculates the difference between them. This allows the information providing device 10 to determine that a region with no POI information but a high level of comprehension (longer stay time and slower travel speed) is a comfortable walking course, such as a riverside. As described above, the information providing device 10 can discover popular local areas even in regions without POI information. In other words, the information providing device 10 can identify the characteristics of a region from the user U's information.

(3-4. Specific Example 4)
A fourth specific example of the information provision process according to the embodiment will be described below. First, the information providing device 10 acquires information about coffee shops in City X based on local information. Next, the information providing device 10 executes a process for calculating a level of understanding using the number of visits and length of stay at coffee shops to identify user UA, who has a deep understanding of City X, and user UB, who has a shallow understanding of City X. At this time, the information providing device 10 can also identify user UA as a local resident and user UB as a tourist using the search history indicated by the behavioral history. Then, the information providing device 10 identifies, among the coffee shops in City X, the coffee shops where user UA, who is a local resident, stays and the coffee shops where user UB, who is a tourist, often stays. As described above, the information providing device 10 can add unique POI information by using local information and search history and comparing the levels of understanding of user UA, who has a deep understanding, and user UB, who has a shallow understanding. In other words, the information providing device 10 can find the characteristics of a region or location from the information of multiple users U.

(3-5. Specific Example 5)
A fifth specific example of the information provision process according to the embodiment will be described below. First, the information providing device 10 executes a comprehension calculation process using the stay points, travel routes, stay times, travel speed, etc. of a user U residing in City X, and calculates the user U's comprehension level for each region. The information providing device 10 also acquires information on points of interest (POI) within City X based on the region information. The information providing device 10 then compares the user U's comprehension levels for each location and calculates the differences. The information providing device 10 can then acquire information on POIs that a user U with a deep understanding of City X has not visited before, and recommend the POIs to the user U. As described above, the information providing device 10 can discover "local places that are actually unknown" even for a user U with a deep understanding of the local area. In other words, the information providing device 10 can find regional characteristics from the user U's information.

(3-6. Specific Example 6)
A sixth specific example of the information provision process according to the embodiment will be described below. First, the information providing device 10 acquires POI information in City Y, a tourist destination, based on regional information. Next, the information providing device 10 executes a comprehension calculation process using the duration of stay, travel speed, and the like to calculate the comprehension levels of tourists who visited City Y for sightseeing during the same period for each region within City Y. Furthermore, the information providing device 10 compares the comprehension levels of the tourists and calculates the difference, thereby identifying that within City Y, region Y2 has a lower comprehension level than region Y1. The information providing device 10 then provides information to user U, a tourist, recommending region Y2, which has a lower comprehension level, when the user visits City Y for the second or subsequent visits. As described above, the information providing device 10 uses regional information and behavioral history, compares the comprehension levels of multiple users U, and provides information on locations with low comprehension levels, thereby increasing the likelihood that the user U will make a new discovery. In other words, the information providing device 10 can identify the characteristics of regions and locations from the information of multiple users U.

[4. Information provision process flow]
The procedure of information processing of the information providing device 10 according to the embodiment will be described with reference to FIG. 8. FIG. 8 is a flowchart showing an example of the flow of the information providing process according to the embodiment. Note that the following steps S101 to S107 may be executed in a different order. Also, some of the following steps S101 to S107 may be omitted.

(4-1. User information collection process)
First, the collection unit 13a of the information providing device 10 executes a user information collection process (step S101). For example, the collection unit 13a collects user information of the user U from the user terminal 20.

(4-2. Regional information collection and processing)
Second, the collection unit 13a of the information providing device 10 executes a local information collection process (step S102). For example, the collection unit 13a refers to the local information database 30 and acquires local information.

(4-3. Understanding Level Calculation Process)
Third, the calculation unit 13b of the information providing device 10 executes an understanding level calculation process (step S103). For example, the calculation unit 13b calculates the understanding level of the user U for each predetermined region from the collected user information and region information.

(4-4. Characteristics Determination Processing)
Fourth, the determination unit 13c of the information providing device 10 executes a characteristic determination process (step S104). For example, the determination unit 13c determines the user characteristics and regional characteristics of the user U from the calculated level of understanding.

(4-5. Provision information generation process)
Fifth, the generation unit 13d of the information providing device 10 executes a provision information generation process (step S105). For example, the generation unit 13d generates provision information to be provided to the user U based on the calculated level of understanding and user characteristics of the user U.

(4-6. Provision information transmission process)
Sixth, the transmitting unit 13e of the information providing device 10 executes a process of transmitting information to be provided (step S106). For example, the transmitting unit 13e transmits the generated information to be provided to the user terminal 20 of the user U.

(4-7. Understanding Level Learning Process)
Seventh, the learning unit 13f of the information providing device 10 executes an understanding level learning process (step S107). For example, the learning unit 13f learns the calculated understanding level of the user U for each predetermined region.

5. Effects of the embodiment
(5-1. Effect 1)
In the process according to the embodiment described above, user information including the user's behavioral history is collected, and a level of understanding indicating the depth of understanding of user U is calculated for each predetermined region based on the collected user information, and information to be provided to user U is generated based on the calculated level of understanding of user U. Therefore, this process can provide useful information to user U.

(5-2. Effect 2)
In the process according to the embodiment described above, area information including location information about predetermined locations included in a predetermined area is collected, and the level of understanding of the user U is calculated for each predetermined area based on the user U's stay locations, travel route, stay time, travel speed, or location information indicated by the behavioral history. Therefore, this process can provide useful information to the user U more effectively.

(5-3. Effect 3)
In the process according to the embodiment described above, the number of visits to the stay points by the user U is calculated, and the more frequently visited the stay points are, the higher the level of understanding of the user U is calculated to be. Therefore, in this process, information about the stay points of the user U is used to more effectively provide useful information to the user U.

(5-4. Effect 4)
In the process according to the embodiment described above, the number of times a user U has used a travel route is calculated, and the more frequently a travel route is used in an area, the higher the level of understanding of the user U is calculated to be. Therefore, in this process, information about the user U's travel route is used to more effectively provide useful information to the user U.

(5-5. Effect 5)
In the process according to the embodiment described above, the time spent by the user U in each predetermined area is calculated, and the longer the time spent in an area, the higher the level of understanding of the user U is calculated to be. Therefore, in this process, information related to the time spent by the user U is used to more effectively provide useful information to the user U.

(5-6. Effect 6)
In the process according to the embodiment described above, the movement speed of the user U for each predetermined area is calculated, and the slower the movement speed of the area, the higher the level of understanding of the user U. Therefore, in this process, information related to the movement speed of the user U is used to more effectively provide useful information to the user U.

(5-7. Effect 7)
In the process according to the present embodiment described above, the visit rate to the specific point for each predetermined region indicated by the point information is calculated, and the higher the visit rate for a region, the higher the calculated level of understanding of the user U. Therefore, in this process, useful information can be provided to the user U more effectively using information about the specific point of the user U.

(5-8. Effect 8)
In the process according to the present embodiment described above, based on the level of understanding of the user U for each predetermined area included in a predetermined range, a high-level user UA with a deep understanding in the range or a low-level user UB with a low understanding in the range is determined. Therefore, in this process, by determining the user characteristics of the user U, useful information can be provided to the user U more effectively.

(5-9. Effect 9)
In the process according to the present embodiment described above, the characteristics of each predetermined region are determined based on the difference in the level of understanding of the user U between the predetermined regions. Therefore, in this process, by determining the regional characteristics from the same user, useful information can be provided to the user U more effectively.

(5-10. Effect 10)
In the process according to the present embodiment described above, characteristics for each predetermined region are determined based on differences in the understanding levels of users U for each predetermined region among multiple users U. Therefore, in this process, by determining regional characteristics from multiple users, useful information can be provided to users U more effectively.

(5-11. Effect 11)
In the process according to the present embodiment described above, if the user U is a high-level understanding user UA, location information about locations included in a predetermined area where the user U's level of understanding is below a predetermined threshold is used to generate information to be provided to the user U. Therefore, in this process, useful information can be more effectively provided to the user UA who has a deep understanding of the predetermined area.

(5-12. Effect 12)
In the process according to the present embodiment described above, when the user U is a low-understanding user UB, location information about locations included in an area where the understanding of a high-understanding user UA within a predetermined range is equal to or greater than a predetermined threshold is used to generate information to be provided to the user U. Therefore, in this process, useful information can be more effectively provided to the user UB who has little understanding of the predetermined area.

[Hardware configuration]
The information providing device 10 according to the embodiment described above is realized, for example, by a computer 1000 configured as shown in Fig. 9. The information providing device 10 will be described below as an example. Fig. 9 is a hardware configuration diagram showing an example of a computer that realizes the functions of the information providing device 10. The computer 1000 has a CPU 1100, a RAM 1200, a ROM 1300, a HDD 1400, a communication interface (I/F) 1500, an input/output interface (I/F) 1600, and a media interface (I/F) 1700.

The CPU 1100 operates based on programs stored in the ROM 1300 or HDD 1400, and controls each component. The ROM 1300 stores a boot program executed by the CPU 1100 when the computer 1000 starts up, as well as programs that depend on the computer 1000's hardware.

HDD 1400 stores programs executed by CPU 1100 and data used by such programs. Communication interface 1500 receives data from other devices via a specified communication network N and sends it to CPU 1100, and transmits data generated by CPU 1100 to other devices via the specified communication network N.

The CPU 1100 controls output devices such as displays and printers, and input devices such as keyboards and mice, via the input/output interface 1600. The CPU 1100 acquires data from input devices via the input/output interface 1600. The CPU 1100 also outputs generated data to output devices via the input/output interface 1600.

Media interface 1700 reads programs or data stored on recording medium 1800 and provides them to CPU 1100 via RAM 1200. CPU 1100 loads the programs from recording medium 1800 onto RAM 1200 via media interface 1700 and executes the loaded programs. Recording medium 1800 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or a PD (Phase Change Rewritable Disc), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory.

For example, when the computer 1000 functions as the information providing device 10 according to the first embodiment, the CPU 1100 of the computer 1000 executes programs loaded onto the RAM 1200 to realize the functions of the control unit 13. The CPU 1100 of the computer 1000 reads and executes these programs from the recording medium 1800, but as another example, the CPU 1100 may obtain these programs from another device via a specified communications network.

〔others〕
Although the embodiments of the present application have been described above, the present invention is not limited to the contents of these embodiments. Furthermore, the above-described components include those that can be easily imagined by a person skilled in the art, those that are substantially the same, and those that are within the so-called equivalent range. Furthermore, the above-described components can be combined as appropriate. Furthermore, various omissions, substitutions, or modifications of the components can be made without departing from the spirit of the above-described embodiments.

Furthermore, among the processes described in the above embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically using known methods. In addition, the information including the processing procedures, specific names, various data, and parameters shown in the above documents and drawings can be changed as desired unless otherwise specified. For example, the various information shown in each drawing is not limited to the information shown.

Furthermore, the components of each device shown in the figure are functional concepts and do not necessarily have to be physically configured as shown. In other words, the specific form of distribution and integration of each device is not limited to that shown, and all or part of them can be functionally or physically distributed and integrated in any unit depending on various loads, usage conditions, etc.

For example, the information providing device 10 described above may be implemented using multiple server computers, and depending on the function, the configuration can be flexibly changed, such as by calling an external platform using an API (Application Programming Interface) or network computing.

Furthermore, the above-described embodiments and variations can be combined as appropriate to the extent that the processing content is not contradictory.

In addition, the above-mentioned "section, module, unit" can be interpreted as "means" or "circuit." For example, a control unit can be interpreted as a control means or a control circuit.

REFERENCE SIGNS LIST 10 Information providing device 11 Communication unit 12 Storage unit 12a User information storage unit 12b Regional information storage unit 12c Understanding level information storage unit 12d Provided information storage unit 13 Control unit 13a Collection unit 13b Calculation unit 13c Determination unit 13d Generation unit 13e Transmission unit 13f Learning unit 20 User terminal 30 Regional information database 100 Information providing system

Claims (12)

a collection unit that collects user information including a user's behavior history and area information including location information related to a predetermined location included in a predetermined area ;
a calculation unit that calculates, for each of the areas, a level of understanding that indicates a depth of understanding according to the user's behavior in the area , based on the behavior history and the location information indicated by the user information;
a generation unit that generates information to be provided to the user based on the level of understanding;
a determination unit that determines whether a user has a high level of understanding in a predetermined range or a user has a low level of understanding in the predetermined range, based on the level of understanding for each of the regions included in the predetermined range;
Equipped with
the determination unit further determines a characteristic for each of the regions based on the difference in the level of understanding between the users for each of the regions, and reflects the determined characteristic in the location information.
An information providing device characterized by: the calculation unit calculates the level of understanding for each area by using at least one of the user's stay points, movement routes, stay times, and movement speeds as the behavior history;
2. The information providing device according to claim 1. the calculation unit calculates the number of visits to the stay point, and calculates the understanding level to be higher for the area including the stay point with a higher number of visits;
3. The information providing device according to claim 2. the calculation unit calculates the number of times the travel route has been used, and calculates the understanding level to be higher for the area that includes the travel route that has been used more frequently.
4. The information providing device according to claim 2 or 3. the calculation unit calculates the staying time for each of the regions, and calculates the understanding level to be higher for the region where the staying time is longer.
5. The information providing device according to claim 2, wherein the information providing device is a device for providing information to a user. the calculation unit calculates the moving speed for each of the regions, and calculates the understanding level to be higher for the region where the moving speed is slower;
6. The information providing device according to claim 2, wherein the information providing device is a device for providing information to a user. the calculation unit calculates a visit rate to the specific point for each of the regions indicated by the point information, and calculates the understanding level to be higher for the region with a higher visit rate;
7. The information providing device according to claim 2, wherein the information providing device is a device for providing information to a user. the determination unit further determines a characteristic for each of the regions based on a difference in the level of understanding between the regions.
8. The information providing device according to claim 7 . When the user is the high understanding level user, the generation unit generates the information to be provided using location information related to locations included in the area in which the understanding level is less than a predetermined threshold within the range.
9. The information providing device according to claim 7 or 8 . When the user is the low understanding level user, the generation unit generates the information to be provided using location information related to locations included in the area in which the understanding level of the high understanding level user in the range is equal to or greater than a predetermined threshold.
9. The information providing device according to claim 7 or 8 . An information providing method executed by an information providing device, comprising:
a collection step of collecting user information including a user's behavior history and area information including location information related to a predetermined location included in the predetermined area ;
a calculation step of calculating, for each of the areas, a level of understanding that indicates a depth of understanding according to the user's behavior in the area , based on the behavior history and the location information indicated by the user information;
a generating step of generating information to be provided to the user based on the level of understanding;
a determining step of determining, based on the level of understanding for each of the regions included in a predetermined range, whether a high level of understanding user has a deep understanding within the range or a low level of understanding user has a low level of understanding within the range;
Including,
the determining step further determines a characteristic for each region based on the difference in the level of understanding between the users for each region, and reflects the determined characteristic in the location information.
1. An information providing method comprising: a collection step of collecting user information including user behavior history and area information including location information regarding predetermined locations included in a predetermined area ;
a calculation step of calculating, for each of the areas, a level of understanding that indicates a depth of understanding according to the user's behavior in the area , based on the behavior history and the location information indicated by the user information;
a generation step of generating information to be provided to the user based on the level of understanding;
a determination step for determining whether a user has a high level of understanding in a predetermined range or a user has a low level of understanding in the predetermined range, based on the level of understanding for each of the regions included in the predetermined range;
on the computer ,
the determining step further determines a characteristic for each of the regions based on the difference in the level of understanding among the users for each of the regions, and reflects the determined characteristic in the location information.
An information providing program characterized by: