Information Processing Apparatus, Information Processing Method, and Information Processing Program

Abstract

To allow for providing information according to analysis of easily bored persons.SOLUTION: An information processing device is provided, comprising a collection unit for collecting user information, a determination unit configured to determine areas of targets of interest to which the users belong in each period of time according to the collected user information, an identification unit configured to identify easily bored users whose targets of interest tend to change easily from among users on the basis of shift in the determined areas of targets of interest, an extraction unit configured to extract features common to the areas of targets of interest to which the easily bored users belong, an estimation unit configured to estimate areas in which the easily bored users may be potentially interested on the basis of the features common to the areas of targets of interest, and a provision unit for providing the easily bored users with information on the areas that may be of potential interest to the easily bored users.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] The present invention relates to an information processing apparatus, an information processing method, and an information processing program.

Background Art

[0002] There is disclosed a technique for making recommendations even for other services that do not have common information (such as user history).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, in the above prior art, it only correlates the metadata assigned to each item handled by each service with each other and presents the items with a high degree of relevance to the user. Although the provision of information about the areas in which the user is interested and related areas is often used, there is room for improving the mode of information provision to the user. For example, information provision based on whether the user is bored has not been performed.

[0005] The present application has been made in view of the above, and an object thereof is to provide information according to the analysis of a person with boredom.

Means for Solving the Problems

[0006] The information processing apparatus according to the present application includes a collection unit that collects user information, a determination unit that determines an area of interest to which the user belongs in each period based on the user information, an extraction unit that extracts features common to the areas, and an estimation unit that estimates an area in which the user potentially has an interest based on the common features.

Effects of the Invention

[0007] According to one aspect of the embodiment, information can be provided according to the analysis of a person with boredom.

Brief Description of the Drawings

[0008]

Figure 1

Figure 2

Figure 3

Figure 4

Figure 5

Figure 6

Figure 7

Figure 8

Figure 9

Mode for Carrying Out the Invention

[0009] Hereinafter, a mode (hereinafter referred to as "embodiment") for implementing the information processing apparatus, information processing method, and information processing program according to the present application will be described in detail with reference to the drawings. Note that the information processing apparatus, information processing method, and information processing program according to the present application are not limited by this embodiment. In the following embodiments, the same parts are denoted by the same reference numerals, and redundant explanations are omitted.

[0010] 〔1. Overview of the Information Processing Method〕 First, referring to FIG. 1, an overview of the information processing method performed by the information processing apparatus according to the embodiment will be described. FIG. 1 is an explanatory diagram showing an overview of the information processing method according to the embodiment. Note that in FIG. 1, the case of providing information according to the analysis of a person with boredom will be described as an example.

[0011] As shown in FIG. 1, the information processing system 1 includes a terminal device 10 and an information providing device 100. The terminal device 10 and the information providing device 100 are connected to each other so as to be communicable with each other by wire or wirelessly via a network N (see FIG. 2). In the present embodiment, the terminal device 10 cooperates with the information providing device 100.

[0012] The terminal device 10 is a smart device such as a smartphone or a tablet used by a user U, and is a portable terminal device capable of communicating with an arbitrary server device via a wireless communication network such as 4G (Generation) or LTE (Long Term Evolution). Further, the terminal device 10 has a screen such as a liquid crystal display and has a screen having a touch panel function, and receives various operations on display data such as content, such as a tap operation, a slide operation, and a scroll operation, from the user U using a finger or a stylus. Note that an operation performed on a region of the screen where content is displayed may be regarded as an operation on the content. Further, the terminal device 10 may be not only a smart device but also an information processing device such as a desktop PC (Personal Computer) or a notebook PC.

[0013] The information providing device 100 is an information processing device that cooperates with the terminal device 10 of each user U and provides an API (Application Programming Interface) service or the like for various applications (hereinafter referred to as apps) and various data to the terminal device 10 of each user U, and is realized by a server device, a cloud system, or the like.

[0014] Further, the information providing apparatus 100 may be an information processing apparatus that provides some kind of Web service online to the terminal device 10 of each user U. For example, as the Web service, the information providing apparatus 100 may provide services such as Internet connection, search service, SNS (Social Networking Service), electronic commerce (EC: Electronic Commerce), electronic payment, online game, online banking, online trading, accommodation / ticket reservation, video / music distribution, news, map, route search, route guidance, route information, operation information, weather forecast, etc. Actually, the information providing apparatus 100 may cooperate with various servers that provide the above Web services and mediate the Web services, or may be in charge of the processing of the Web services.

[0015] Note that the information providing apparatus 100 can acquire user information regarding the user U. For example, the information providing apparatus 100 acquires information regarding the attributes of the user U such as the gender, age, and residential area of the user U. Then, the information providing apparatus 100 stores and manages the information regarding the attributes of the user U together with the identification information (such as user ID) indicating the user U.

[0016] In addition, the information providing apparatus 100 acquires various types of history information (log data) indicating the actions of the user U from the terminal device 10 of the user U or from various servers or the like based on the user ID or the like. For example, the information providing apparatus 100 acquires a location history, which is a history of the location and time of the user U, from the terminal device 10. In addition, the information providing apparatus 100 acquires a search history, which is a history of search queries input by the user U, from a search server (search engine). In addition, the information providing apparatus 100 acquires a browsing history, which is a history of content browsed by the user U, from a content server. In addition, the information providing apparatus 100 acquires a purchase history (settlement history), which is a history of product purchases and settlement processing of the user U, from an e-commerce server or a settlement processing server. In addition, the information providing apparatus 100 may acquire a listing history and a sales history, which are histories of the user U's listings on the marketplace, from an e-commerce server or a settlement server. In addition, the information providing apparatus 100 acquires a posting history, which is a history of the user U's posts, from a posting server or an SNS server that provides a word-of-mouth posting service.

[0017] The information providing apparatus 100 according to the present embodiment provides information according to the analysis of users with a tendency to get bored. In the present embodiment, the information providing apparatus 100 analyzes the common points showing interest in a certain area (field, target, matter), and finds an area in which users with a tendency to get bored can become enthusiastic although they are not currently aware of it.

[0018] A user with a tendency to get bored (a user with a boredom tendency) is, for example, a person whose period of interest or concern in a specific area (field, target, matter) is below the average, but whose number of searches and expenses during that period are above the average. That is, it is a person who becomes enthusiastic and concentratedly collects information and consumes for a certain period in a specific area but does not last long. On the other hand, a fan user is a person who continuously becomes enthusiastic and concentratedly collects information and consumes in a specific area.

[0019] [1-1. Estimation and Information Provision for Users with a Tendency to Get Bored] As shown in FIG. 1, the information providing apparatus 100 collects user information (search queries, behavior history, purchase history, etc.) regarding the user U via the network N (see FIG. 2) (step S1). For example, the information providing apparatus 100 directly or indirectly collects the location information, attribute information, history information, etc. of the user U via the network N (see FIG. 2). Further, when the information providing apparatus 100 receives requests such as searches and e-commerce transactions from the terminal device 10 of each user U, it may acquire search queries, purchase information, location information, etc., and accumulate them as history information.

[0020] Subsequently, the information providing apparatus 100 determines, for each user, an area in which the user has shown interest or concern from the user information (search queries, behavior history, purchase history, etc.) (step S2). For the determination, a determination model for each area by machine learning can be used. For example, the information providing apparatus 100 learns the characteristics of the user information (search queries, behavior history, purchase history, etc.) of users who are surely in each area (appropriately, "belonging users"), and constructs and uses a model that determines whether the user is engrossed in a specific area when the user information is input. Further, the information providing apparatus 100 may determine the area in which interest or concern has been shown based on rules.

[0021] Subsequently, the information providing apparatus 100 identifies users (users with boredom tendency) whose interests are likely to change based on the period and degree to which the user U has been interested or concerned about each area (step S3). A user with boredom tendency is, for example, a person whose interest does not persist in each area (a person who does not continue a specific hobby). For example, the information providing apparatus 100 identifies, as users with boredom tendency, users whose frequency of interest transfer from one area to another is high (the period of having an interest in a specific area is shorter than the average period).

[0022] At this time, the information providing apparatus 100 may estimate the average value (or median value) of the period (the period of being engrossed) of having an interest in each area, and identify the user U with a period shorter than the estimated average value as a user (user with boredom tendency) whose interests are likely to change.

[0023] Subsequently, the information providing apparatus 100 extracts common features of areas in which the jaded users have shown interest based on the user information (step S4). For example, the information providing apparatus 100 extracts common objects of interest included in a plurality of areas in which the jaded user U1 has shown interest every month (intra-user feature extraction). Further, the information providing apparatus 100 extracts common objects of interest included in areas in which a plurality of jaded users U1 to U5 have shown interest every month (inter-user feature extraction).

[0024] Also, the information providing apparatus 100 may consider trends (the chronological order of trendy words and areas), etc. Note that the information providing apparatus 100 analyzes, for example, in a Markov process on the vector space of Word2vec. Here, Word2vec is merely an example of natural language processing.

[0025] At this time, the information providing apparatus 100 may exclude the features of the areas of the user information of the fan users and extract the common features of the areas in which the jaded users have shown interest. That is, the information providing apparatus 100 can also estimate the range of areas in which the jaded users are interested with higher accuracy by extracting the common features specific to the jaded users, and can also provide the information regarding the estimated areas to the jaded users.

[0026] Subsequently, the information providing apparatus 100 estimates an area in which the user U is potentially interested based on the common features of the extracted areas (step S5). For example, when the objects in which the jaded user U1 has shown interest in the past are "watch a of brand A" and "watch b of brand B", the information providing apparatus 100 estimates "brand C" that manufactures and sells "watch c" as an area in which the jaded user U1 is potentially interested from the common feature of "watch". Further, when the common object of interest in which a plurality of jaded users U1 to U3 have shown interest in the past is "knife", and the areas to which the jaded users U1 to U3 belong are "outdoor" for U1, "cooking" for U2, and "DIY" for U3, respectively, the information providing apparatus 100 estimates the areas in which the jaded user U1 is potentially interested as "cooking" and "DIY".

[0027] Then, the information providing apparatus 100 generates recommendation information for the user U based on the user information of the user U, and provides the generated recommendation information (step S6). For example, the information providing apparatus 100 generates recommendation information regarding an area (an area in which the user may be interested) that the user U is potentially interested in based on the user information of the user U, and provides the recommendation information. At this time, the information providing apparatus 100 may provide the recommendation information at a timing when the user's interest in the currently interesting area has decreased.

[0028] In addition, the information providing apparatus 100 learns common features of areas in which users with a tendency to get bored have shown interest (step S7). For example, the information providing apparatus 100 learns common objects of interest included in a plurality of areas in which the user U has shown interest every month using a machine learning model, and estimates an area that a user with a tendency to get bored is potentially interested in based on the learning result.

[0029] As described above, in the present embodiment, the information providing apparatus 100 collects user information, determines a history (change in interest) of areas in which the user has been interested from the user information, and identifies users (users with a tendency to get bored) whose interests are likely to change. Next, the information providing apparatus 100 extracts common features of the areas of interest from the user information of the users with a tendency to get bored. Then, the information providing apparatus 100 estimates an area that a user with a tendency to get bored is potentially interested in from the extracted features, and provides recommendation information to the users with a tendency to get bored. Therefore, the information providing apparatus 100 can provide information according to the analysis of users with a tendency to get bored.

[0030] Here, the importance of analyzing users with a tendency to get bored will be explained. First, users with a tendency to get bored are those who concentrate certain actions (such as usage amount or purchases) but shift to another area for a short period as a higher-level concept. At this time, no matter how much the interests of users with a tendency to get bored change, they should change with a certain tendency. Therefore, analyzing this tendency and making effective recommendations for users with a tendency to get bored is highly important in marketing. For example, it is also possible to narrow down the features based on the information collected from users to those specific to users with a tendency to get bored and use them as ideas for collaborations or product planning. Also, the areas that users with a tendency to get bored are interested in may be areas that many people are similarly interested in, and it can also be used to estimate areas that will become popular in the future.

[0031] Next, the importance of analyzing the common points of the objects of interest will be explained. For example, actions specific to fans compared to users with a tendency to get bored may be attractions that users with a tendency to get bored have not yet noticed in that area. At this time, if the information providing device 100 extracts things that fans are interested in but users with a tendency to get bored are not interested in, it can generate recommendation information that leads to the discovery of new attractions to appeal to users with a tendency to get bored.

[0032] Also, areas specific to users with a tendency to get bored compared to fans may have unexpected combinations that fans have not yet noticed. At this time, if the information providing device 100 extracts things that users with a tendency to get bored are interested in but fans are not interested in, it can create distant combination plans. Note that, if necessary, trimming by relevance may also be performed to exclude things that are too close.

[0033] Furthermore, the information providing device 100 can analyze the common points of the objects of interest as described above, estimate the range of interests of each user or all users, and make effective recommendations for users regardless of whether they are users with a tendency to get bored or fans.

[0034] 〔2. Configuration Example of Information Processing System〕 Next, with reference to FIG. 2, the configuration of the information processing system 1 including the information providing apparatus 100 according to the embodiment will be described. FIG. 2 is a diagram showing a configuration example of the information processing system 1 according to the embodiment. As shown in FIG. 2, the information processing system 1 according to the embodiment includes a terminal device 10 and an information providing apparatus 100. These various devices are communicably connected by wire or wirelessly via a network N. The network N is, for example, a LAN (Local Area Network) or a WAN (Wide Area Network) such as the Internet.

[0035] Also, the number of each device included in the information processing system 1 shown in FIG. 2 is not limited to that shown. For example, in FIG. 2, only one terminal device 10 is shown for simplicity of illustration, but this is merely an example and not limiting, and two or more may be used.

[0036] The terminal device 10 is an information processing device used by the user U. For example, the terminal device 10 is a smart device such as a smartphone or a tablet terminal, a feature phone, a PC (Personal Computer), a PDA (Personal Digital Assistant), a game machine or an AV device having a communication function, a car navigation system, a wearable device such as a smartwatch or a head-mounted display, a smart glass, or the like.

[0037] Also, such a terminal device 10 can be connected to the network N via a wireless communication network such as LTE (Long Term Evolution), 4G (4th Generation), 5G (5th Generation: the 5th generation mobile communication system) or short-range wireless communication such as Bluetooth (registered trademark) or wireless LAN (Local Area Network) and communicate with the information providing apparatus 100.

[0038] The information providing device 100 is, for example, a PC, a server device, or a mainframe or a workstation, etc. Note that the information providing device 100 may be realized by cloud computing.

[0039] [3. Configuration Example of Terminal Device] Next, with reference to FIG. 3, the configuration of the terminal device 10 will be described. FIG. 3 is a diagram showing a configuration example of the terminal device 10. As shown in FIG. 3, the terminal device 10 includes a communication unit 11, a display unit 12, an input unit 13, a positioning unit 14, a sensor unit 20, a control unit 30 (controller), and a storage unit 40.

[0040] (Communication Unit 11) The communication unit 11 is connected to the network N (see FIG. 2) by wire or wirelessly, and transmits and receives information to and from the information providing device 100 via the network N. For example, the communication unit 11 is realized by a NIC (Network Interface Card), an antenna, etc.

[0041] (Display Unit 12) The display unit 12 is a display device that displays various information such as position information. For example, the display unit 12 is a liquid crystal display (LCD) or an organic EL display (Organic Electro-Luminescent Display). Also, the display unit 12 is a touch panel type display, but is not limited thereto.

[0042] (Input Unit 13) The input unit 13 is an input device that receives various operations from the user U. For example, the input unit 13 has buttons for inputting characters, numbers, etc. Note that the input unit 13 may be an input / output port (I / O port), a USB (Universal Serial Bus) port, etc. Also, when the display unit 12 is a touch panel type display, a part of the display unit 12 functions as the input unit 13. Also, the input unit 13 may be a microphone that receives voice input from the user U. The microphone may be wireless.

[0043] (Positioning unit 14) The positioning unit 14 receives signals (radio waves) sent from GPS (Global Positioning System) satellites, and based on the received signals, acquires position information (for example, latitude and longitude) indicating the current position of the terminal device 10, which is the device itself. That is, the positioning unit 14 measures the position of the terminal device 10. Note that GPS is just an example of GNSS (Global Navigation Satellite System).

[0044] In addition to GPS, the positioning unit 14 can measure the position by various methods. For example, as an auxiliary positioning means for position correction and the like, the positioning unit 14 may measure the position by using various communication functions of the terminal device 10 as follows.

[0045] (Wi-Fi positioning) For example, the positioning unit 14 measures the position of the terminal device 10 by using the Wi-Fi (registered trademark) communication function of the terminal device 10 or the communication networks provided by each communication company. Specifically, the positioning unit 14 performs Wi-Fi communication and measures the distance to nearby base stations or access points to measure the position of the terminal device 10.

[0046] (Beacon positioning) In addition, the positioning unit 14 may measure the position by using the Bluetooth (registered trademark) function of the terminal device 10. For example, the positioning unit 14 measures the position of the terminal device 10 by connecting to a beacon transmitter connected by the Bluetooth (registered trademark) function.

[0047] (Geomagnetic positioning) In addition, the positioning unit 14 measures the position of the terminal device 10 based on the geomagnetic pattern of a structure measured in advance and the geomagnetic sensor provided in the terminal device 10.

[0048] (RFID positioning) Also, for example, when the terminal device 10 has a function equivalent to that of an RFID (Radio Frequency Identification) tag used at a station ticket gate, a store, etc., or when it has a function to read an RFID tag, the location where it is used is recorded together with the information on which settlement, etc. has been performed by the terminal device 10. The positioning unit 14 may position the location of the terminal device 10 by acquiring such information. Also, the location may be positioned by an optical sensor, an infrared sensor, etc. provided in the terminal device 10.

[0049] The positioning unit 14 may, if necessary, position the location of the terminal device 10 using one or a combination of the above-described positioning means.

[0050] (Sensor unit 20) The sensor unit 20 includes various sensors mounted on or connected to the terminal device 10. Note that the connection may be either a wired connection or a wireless connection. For example, the sensors may be detection devices other than the terminal device 10, such as wearable devices or wireless devices. In the example shown in FIG. 3, the sensor unit 20 includes an acceleration sensor 21, a gyro sensor 22, a pressure sensor 23, a temperature sensor 24, a sound sensor 25, a light sensor 26, a magnetic sensor 27, and an image sensor (camera) 28.

[0051] Note that the above-described sensors 21 to 28 are merely examples and are not limiting. That is, the sensor unit 20 may be configured to include some of the sensors 21 to 28, or may include other sensors such as a humidity sensor in addition to or instead of the sensors 21 to 28.

[0052] The acceleration sensor 21 is, for example, a three-axis acceleration sensor, and detects physical movements of the terminal device 10 such as the moving direction, speed, and acceleration of the terminal device 10. The gyro sensor 22 detects physical movements of the terminal device 10 such as the inclination in three-axis directions based on the angular velocity, etc. of the terminal device 10. The pressure sensor 23 detects, for example, the atmospheric pressure around the terminal device 10.

[0053] Since the terminal device 10 is equipped with the above-described acceleration sensor 21, gyro sensor 22, barometric pressure sensor 23, etc., it becomes possible to measure the position of the terminal device 10 using technologies such as pedestrian dead reckoning (PDR) that utilize these sensors 21 to 23, etc. As a result, it becomes possible to obtain position information indoors, which is difficult to obtain with a positioning system such as GPS.

[0054] For example, with a pedometer that uses the acceleration sensor 21, the number of steps, walking speed, and distance walked can be calculated. Also, by using the gyro sensor 22, the direction of travel, line of sight direction, and body tilt of the user U can be known. Further, from the barometric pressure detected by the barometric pressure sensor 23, the altitude and floor number where the terminal device 10 of the user U is located can also be known.

[0055] The temperature sensor 24 detects, for example, the temperature around the terminal device 10. The sound sensor 25 detects, for example, the sound around the terminal device 10. The light sensor 26 detects the illuminance around the terminal device 10. The magnetic sensor 27 detects, for example, the geomagnetism around the terminal device 10. The image sensor 28 captures an image of the surroundings of the terminal device 10.

[0056] The above-described barometric pressure sensor 23, temperature sensor 24, sound sensor 25, light sensor 26, and image sensor 28 can detect the environment and situation around the terminal device 10 by detecting barometric pressure, temperature, sound, illuminance, or capturing an image of the surroundings, respectively. Also, it becomes possible to improve the accuracy of the position information of the terminal device 10 from the environment and situation around the terminal device 10.

[0057] (Control unit 30) The control unit 30 includes, for example, a microcomputer having a CPU (Central Processing Unit), a ROM (Read Only Memory), a RAM, input / output ports, etc., and various circuits. Further, the control unit 30 may be configured by hardware such as an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array). The control unit 30 includes a transmission unit 31, a reception unit 32, and a processing unit 33.

[0058] (Transmission unit 31) The transmission unit 31 can transmit, via the communication unit 11, various information input by the user U using the input unit 13, various information detected by the sensors 21 to 28 mounted on or connected to the terminal device 10, the position information of the terminal device 10 positioned by the positioning unit 14, and the like to the information providing device 100.

[0059] (Reception unit 32) The reception unit 32 can receive, via the communication unit 11, various information provided from the information providing device 100 and requests for various information from the information providing device 100.

[0060] (Processing unit 33) The processing unit 33 controls the entire terminal device 10 including the display unit 12 and the like. For example, the processing unit 33 can output and display various information transmitted by the transmission unit 31 and various information from the information providing device 100 received by the reception unit 32 to the display unit 12.

[0061] (Storage unit 40) The storage unit 40 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory, or a storage device such as an HDD (Hard Disk Drive), an SSD (Solid State Drive), or an optical disk. Various programs, various data, and the like are stored in such a storage unit 40.

[0062] [4. Configuration Example of Information Providing Device] Next, with reference to FIG. 4, the configuration of the information providing device 100 according to the embodiment will be described. FIG. 4 is a diagram showing a configuration example of the information providing device 100 according to the embodiment. As shown in FIG. 4, the information providing device 100 includes a communication unit 110, a storage unit 120, and a control unit 130.

[0063] (Communication Unit 110) The communication unit 110 is realized by, for example, a NIC (Network Interface Card) or the like. Further, the communication unit 110 is connected to the network N (see FIG. 2) by wire or wirelessly.

[0064] (Storage Unit 120) The storage unit 120 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory, or a storage device such as an HDD, an SSD, or an optical disk. As shown in FIG. 4, the storage unit 120 includes a user information database 121, a history information database 122, and a boredom information database 123.

[0065] (User Information Database 121) The user information database 121 stores user information regarding the user U. For example, the user information database 121 stores various information such as the attributes of the user U. FIG. 5 is a diagram showing an example of the user information database 121. In the example shown in FIG. 5, the user information database 121 has items such as "User ID (Identifier)", "Age", "Gender", "Home", "Workplace", and "Interests".

[0066] "User ID" indicates identification information for identifying the user U. Note that the "User ID" may be the contact information (phone number, email address, etc.) of the user U, or may be identification information for identifying the terminal device 10 of the user U.

[0067] In addition, "Age" indicates the age of user U identified by the user ID. Note that "Age" may be information indicating the specific age of user U (e.g., 35 years old, etc.), or information indicating the age group of user U (e.g., in their 30s, etc.). Alternatively, "Age" may be information indicating the date of birth of user U, or information indicating the generation of user U (e.g., born in the 1980s, etc.). Also, "Gender" indicates the gender of user U identified by the user ID.

[0068] In addition, "Home" indicates the location information of the home of user U identified by the user ID. Note that in the example shown in FIG. 5, "Home" is illustrated with an abstract symbol such as "LC11", but it may also be latitude and longitude information, etc. Also, for example, "Home" may be a regional name or an address.

[0069] In addition, "Workplace" indicates the location information of the workplace of user U (school in the case of students) identified by the user ID. Note that in the example shown in FIG. 5, "Workplace" is illustrated with an abstract symbol such as "LC12", but it may also be latitude and longitude information, etc. Also, for example, "Workplace" may be a regional name or an address.

[0070] In addition, "Interest" indicates the interest of user U identified by the user ID. That is, "Interest" indicates the object that user U identified by the user ID is highly interested in. For example, "Interest" may be a search query (keyword) etc. that user U entered into a search engine and searched for. Note that in the example shown in FIG. 5, "Interest" is illustrated one by one for each user U, but it may also be multiple.

[0071] For example, in the example shown in FIG. 5, it is shown that the age of user U identified by the user ID "U1" is "in their 20s" and the gender is "male". Also, for example, it is shown that the home of user U identified by the user ID "U1" is "LC11". Also, for example, it is shown that the workplace of user U identified by the user ID "U1" is "LC12". Also, for example, it is shown that user U identified by the user ID "U1" is interested in "sports".

[0072] Here, in the example shown in FIG. 5, abstract values such as "U1", "LC11", and "LC12" are used for illustration, but it is assumed that information such as specific character strings and numerical values is stored in "U1", "LC11", and "LC12". Hereinafter, in the figures regarding other information, abstract values may be illustrated in some cases.

[0073] Note that the user information database 121 is not limited to the above, and may store various information according to the purpose. For example, the user information database 121 may store various information regarding the terminal device 10 of the user U. Also, the user information database 121 may store information regarding attributes such as the demographics (demographic attributes), psychographics (psychological attributes), geographics (geographical attributes), and behavioral (behavioral attributes) of the user U. For example, the user information database 121 may store information such as name, family composition, place of origin (local area), occupation, position, income, qualifications, housing form (detached house, condominium, etc.), presence or absence of a car, commuting / going-to-school time, commuting / going-to-school route, commuter pass section (station, line, etc.), stations with high usage frequency (other than the nearest stations to the home / workplace), hobbies (place, time zone, etc.), interests, and lifestyle.

[0074] (History Information Database 122) The history information database 122 stores various information regarding history information (log data) indicating the actions of the user U. FIG. 6 is a diagram showing an example of the history information database 122. In the example shown in FIG. 6, the history information database 122 has items such as "user ID", "location history", "search history", "browsing history", "purchase history", and "posting history".

[0075] "User ID" indicates identification information for identifying user U. Also, "location history" indicates a location history that is a history of the location and movement of user U. Also, "search history" indicates a search history that is a history of search queries input by user U. Also, "browsing history" indicates a browsing history that is a history of content browsed by user U. Also, "purchase history" indicates a purchase history that is a history of purchases made by user U. Also, "posting history" indicates a posting history that is a history of posts made by user U. Note that the "posting history" may include questions about the possessions of user U.

[0076] For example, in the example shown in FIG. 6, user U identified by user ID "U1" moved as per "location history #1", searched as per "search history #1", browsed content as per "browsing history #1", purchased a predetermined product or the like at a predetermined store or the like as per "purchase history #1", and posted as per "posting history".

[0077] Here, in the example shown in FIG. 6, abstract values such as "U1", "location history #1", "search history #1", "browsing history #1", "purchase history #1", and "posting history #1" are used for illustration, but it is assumed that specific information such as character strings and numerical values is stored in "U1", "location history #1", "search history #1", "browsing history #1", "purchase history #1", and "posting history #1".

[0078] Note that the history information database 122 is not limited to the above and may store various information according to the purpose. For example, the history information database 122 may store the usage history of a predetermined service of user U. Also, the history information database 122 may store the store visit history or facility visit history of the actual store of user U. Also, the history information database 122 may store the settlement history or the like in settlement (electronic settlement) using the terminal device 10 of user U.

[0079] (Boredom Information Database 123) The boredom information database 123 stores various information regarding whether user U is a person with a tendency to get bored. FIG. 7 is a diagram showing an example of the boredom information database 123. In the example shown in FIG. 7, the boredom information database 123 has items such as "user ID", "area", "category", "degree of interest", "period of interest", and "boredom tendency".

[0080] "User ID" indicates identification information for identifying user U. Also, "area" indicates the area in which user U had an interest. Also, "category" indicates the category (classification, genre) of the area in which user U had an interest. Also, "degree of interest" indicates the degree of interest that user U had in the area. Also, "period of interest" indicates the period during which user U had an interest in the area. Also, "boredom tendency", based on the above items, indicates whether user U is a person with a tendency to get bored (〇) or not (×). Note that in reality, "boredom tendency" may be expressed as a numerical value (1, 2, 3,...) or magnitude (large / medium / small, high / middle / low, etc.).

[0081] For example, in the example shown in FIG. 7, user U identified by user ID "U1" may have had an interest in "areas #1A to #1E", those areas belong to "categories #1A to #1E", the degree of that interest is "degrees of interest #1A to #1E", the period during which the interest was held is "periods of interest #1A to #1E", indicating that it is presumed to be a "person with a tendency to get bored" from these contents.

[0082] Here, in the example shown in FIG. 7, it is illustrated using abstract values such as "U1", "areas #1A to #1E", "categories #1A to #1E", "degrees of interest #1A to #1E", and "periods of interest #1A to #1E", but it is assumed that information such as specific character strings and numerical values is stored in "U1", "areas #1A to #1E", "categories #1A to #1E", "degrees of interest #1A to #1E", and "periods of interest #1A to #1E".

[0083] Note that the boredom information database 123 may store various types of information according to the purpose, not limited to the above. For example, the boredom information database 123 may store an estimation model used for estimation and the like. Further, the boredom information database 123 may store information regarding the commonalities or differences with the user information of users (non-boring people) whose interests similar to those of the user U do not change easily.

[0084] (Control unit 130) Returning to FIG. 4 and continuing the explanation. The control unit 130 is a controller, and is realized, for example, by various programs (corresponding to an example of an information processing program) stored in the internal storage device of the information providing apparatus 100 being executed with a storage area such as a RAM as a work area by a CPU (Central Processing Unit), an MPU (Micro Processing Unit), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or the like. In the example shown in FIG. 4, the control unit 130 includes a collection unit 131, a determination unit 132, a specification unit 133, an extraction unit 134, an estimation unit 135, a provision unit 136, and a learning unit 137.

[0085] (Collection unit 131) The collection unit 131 collects user information. First, the collection unit 131 acquires a search query input by the user U. For example, when the user U inputs a search query to a search engine or the like to perform a keyword search, the collection unit 131 acquires the search query via the communication unit 110. That is, the collection unit 131 acquires the keyword input by the user U to the search window of the search engine, site, or application via the communication unit 110.

[0086] In addition, the collection unit 131 acquires user information regarding the user U via the communication unit 110. For example, the collection unit 131 acquires identification information (such as a user ID) indicating the user U, the location information of the user U, the attribute information of the user U, etc. from the terminal device 10 of the user U. Further, the collection unit 131 may acquire identification information indicating the user U, the attribute information of the user U, etc. at the time of user registration of the user U. Then, the collection unit 131 registers the user information in the user information database 121 of the storage unit 120.

[0087] In addition, the collection unit 131 acquires various history information (log data) indicating the actions of the user U via the communication unit 110. For example, the collection unit 131 acquires various history information indicating the actions of the user U from the terminal device 10 of the user U or from various servers etc. based on the user ID etc. Then, the collection unit 131 registers the various history information in the history information database 122 of the storage unit 120.

[0088] In this way, the collection unit 131 collects user information (search queries, action histories, purchase histories, etc.) for each user via the communication unit 110.

[0089] (Determination unit 132) The determination unit 132 determines the area of interest to which the user belongs in each period based on the user information. For example, the determination unit 132 uses the search query, action history, or purchase history of the user U as the user information to determine the area of interest to which the user U belongs in each period.

[0090] In addition, the determination unit 132 determines the area of interest to which the user U belongs in each period, using a model learned to determine whether a user belongs to each area of interest based on the user information of the users belonging to each area of interest. That is, a model such as a DNN (Deep Neural Network) learned using the user information of users who are certain to belong to each area of interest (belonging users) as learning data is used to determine the area of interest to which the user U belongs in each period. At this time, the information processing apparatus 100 may determine whether the user U belongs to a specific area based on the similarity between the user information of the user U to be determined and the user information of the belonging users.

[0091] Here, when determining the similarity, the information providing apparatus 100 may calculate a score indicating the similarity based on rules, and determine whether the user U belongs to a specific area when the calculated similarity exceeds a predetermined threshold.

[0092] (Specific identification unit 133) The specific identification unit 133 identifies users with a high likelihood of getting bored, whose area of interest is likely to change, based on the transition of the areas of interest to which the users belong in each period. For example, the specific identification unit 133 calculates the average period for which a user belongs to each area of interest, and identifies as users with a high likelihood of getting bored those users among them whose period of belonging to each area is shorter than the average period.

[0093] Here, the average period calculated by the specific identification unit 133 is the average value of the belonging periods of each area determined by the determination unit 132. In addition, the specific identification unit 133 may identify the user U as a user with a high likelihood of getting bored when all the belonging periods of the area determined to be the area to which the user U belongs are shorter than the average period, or may identify the user U as a user with a high likelihood of getting bored when a predetermined number of belonging periods are shorter than the average period, or may identify users with a high likelihood of getting bored by comparing with the average period of all the set areas. Furthermore, the specific identification unit 133 can also identify as users with a high likelihood of getting bored those users among the users who satisfy the above-mentioned belonging period conditions and who have made an expenditure of a predetermined amount within a certain period.

[0094] Then, the specific part 133 registers various boredom information in the boredom information database 123 of the storage part 120.

[0095] (Extraction part 134) The extraction part 134 extracts features common to the area of interest to which the user belongs. For example, the extraction part 134 extracts features common to the area of interest to which the bored user belongs. Also, the extraction part 134 extracts features common to the areas within each user or between each user.

[0096] Here, an example of feature extraction within a user will be described. When the extraction part 134 takes "watch a of brand A" and "watch b of brand B" as the objects that the bored user U1 has shown interest in the past, the extraction part 134 extracts the common feature of "watch" of the bored user U1. Also, an example of feature extraction between users will be described. When the extraction part 134 takes "survival knife" for user U1, "cooking knife" for user U2, and "tool knife" for user U3 as the objects that a plurality of bored users U1 to U3 have shown interest in the past, the extraction part 134 extracts the common feature of "knife".

[0097] Also, the extraction part 134 uses the vectorized user information to extract features common to the areas within each user or between each user. For example, the extraction part 134 performs natural language processing by Word2vec to vectorize the user information and uses it to extract features common to the area of interest of the bored user.

[0098] Also, the extraction part 134 excludes the features of the area to which the fan user who has continuously shown interest belongs and extracts the features common to the area of interest. For example, the extraction part 134 excludes the features during a specific campaign or event as features that can also be seen in fan users and other users, and extracts the features unique to the bored user.

[0099] (Estimation part 135) The estimation unit 135 estimates the area that the user may potentially be interested in based on the features common to the areas of interest to which the user belongs. For example, the estimation unit 135 estimates the area that the boredom-prone user may potentially be interested in based on the features common to the areas of interest to which the boredom-prone user belongs. Further, the estimation unit 135 estimates the area that the user may potentially be interested in based on the features common to the areas within each user or among the users.

[0100] Here, an example of area estimation within a user will be described. When the common feature of the boredom-prone user U1 is "watch", the estimation unit 135 estimates that the user U1 may potentially be interested in the area of "Brand C" that manufactures and sells "watch c" with no search history or purchase history in the past. Also, an example of area estimation among users will be described. When the common feature among a plurality of boredom-prone users U1 to U3 is "knife" as the object that they have shown interest in the past, where user U1 is interested in "survival knife" (area: outdoors), user U2 is interested in "cooking knife" (area: cooking), and user U3 is interested in "tool knife" (area: DIY), the estimation unit 135 estimates that the user U1 may potentially be interested in the areas of "outdoors" and "DIY" with no search history or purchase history.

[0101] Furthermore, the extraction unit 134 may estimate the area that each user may potentially be interested in using the learned model learned by the learning unit 137.

[0102] (Provision unit 136) The provision unit 136 provides information regarding the area that the boredom-prone user may potentially be interested in to the boredom-prone user. For example, the provision unit 136 provides recommendation information regarding the area that the boredom-prone user may potentially be interested in to the boredom-prone user.

[0103] In addition, the providing unit 136 provides information on areas that the satiated user potentially has an interest in at the timing when the interest in the area currently showing interest decreases for the satiated user. For example, the providing unit 136 provides recommendation information on areas that the satiated user potentially has an interest in at the timing when the search frequency of the area currently showing interest for the satiated user has decreased. Further, the providing unit 136 may provide information on areas that the user potentially has an interest in to fan users and other users.

[0104] (Learning unit 137) The learning unit 137 learns the features common to the areas of interest within each user or among users. For example, when the learning unit 137 inputs the user information of the satiated user, it learns using a model such as a DNN so as to output information on areas that the satiated user potentially has an interest in. At this time, the learning unit 137 may perform learning by backpropagation or the like.

[0105] [5. Processing procedure] Next, the processing procedure by the information providing apparatus 100 according to the embodiment will be described with reference to FIG. 8. FIG. 8 is a flowchart showing the processing procedure according to the embodiment. Note that the following processing procedure is repeatedly executed by the control unit 130 of the information providing apparatus 100. Also, the following processing procedure can be executed in a different order. Further, some of the following processing procedures may be omitted.

[0106] As shown in FIG. 8, the collection unit 131 of the information providing apparatus 100 collects user information (search query, action history, purchase history, etc.) for each user via the communication unit 110 (step S101).

[0107] Subsequently, the determination unit 132 of the information providing apparatus 100 sets the area in which the user to be determined shows interest or concern (step S102).

[0108] Subsequently, the determination unit 132 of the information providing apparatus 100 determines, for each user, an area in which the user has shown interest or concern from user information (search queries, behavior history, purchase history, etc.) (step S103).

[0109] Subsequently, the specifying unit 133 of the information providing apparatus 100 specifies users (users with a tendency to get bored) whose interests are likely to change from the determination results of the period and degree of interest (step S104).

[0110] Subsequently, the extraction unit 134 of the information providing apparatus 100 extracts common features of the areas in which the users with a tendency to get bored have shown interest from the user information of the users with a tendency to get bored (step S105).

[0111] Subsequently, the estimation unit 135 of the information providing apparatus 100 estimates areas in which users with a tendency to get bored may potentially have an interest (step S106).

[0112] Subsequently, the providing unit 136 of the information providing apparatus 100 generates recommendation information for users with a tendency to get bored and provides information to the users with a tendency to get bored (step S107).

[0113] Subsequently, the learning unit 137 of the information providing apparatus 100 learns the common features of the areas in which users with a tendency to get bored have shown interest (step S108).

[0114] 〔6. Modification Example〕 The terminal device 10 and the information providing apparatus 100 described above may be implemented in various different forms other than the above-described embodiment. Therefore, modification examples of the embodiment will be described below.

[0115] In the above-described embodiment, some or all of the processes executed by the information providing apparatus 100 may actually be executed by the terminal apparatus 10. For example, the process may be completed in a stand-alone manner (by the terminal apparatus 10 alone). In this case, it is assumed that the terminal apparatus 10 has the functions of the information providing apparatus 100 in the above-described embodiment. Further, in the above-described embodiment, since the terminal apparatus 10 is in cooperation with the information providing apparatus 100, from the viewpoint of the user U, the processes of the information providing apparatus 100 also seem to be executed by the terminal apparatus 10. That is, from another viewpoint, it can be said that the terminal apparatus 10 includes the information providing apparatus 100.

[0116] Further, in the above-described embodiment, the areas in which the user U is interested may be famous people, works (literature, art, entertainment, etc.), sports, games, food and drink, stores, facilities, vehicles, countries, regions, and the like.

[0117] Further, in the above-described embodiment, the areas in which the user U is interested are not limited to the areas of hobbies, but may be business, school subjects, learning, etc. Further, they may be companies, schools, cram schools, and the like.

[0118] Further, in the above-described embodiment, the areas in which the user U is interested may be matters related to beauty, health, and diet. Further, they may be matters related to diseases, medicines, hospitals, and the like.

[0119] 〔7. Effects〕 As described above, the information processing apparatus (the terminal apparatus 10 and the information providing apparatus 100) according to the present application includes a collection unit 131 that collects user information, a determination unit 132 that determines the area of the object of interest to which the user belongs in each period based on the user information, an extraction unit 134 that extracts features common to the areas of the object of interest, and an estimation unit 135 that estimates the area in which the user potentially has an interest based on the common features.

[0120] In addition, the information processing apparatus according to the present application further includes a specifying unit 133 that specifies users with a boredom tendency, among users, whose objects of interest are likely to change, based on the transition of the determined regions of interest, and a providing unit 136 that provides information regarding regions that the boredom-prone users potentially have an interest in, to the boredom-prone users.

[0121] In addition, the information processing apparatus according to the present application further includes a learning unit that learns features common to the regions of interest within each user or among users. Further, the estimating unit 135 estimates regions that each user potentially has an interest in, based on the learning result.

[0122] In addition, the determination unit 132 determines the regions of interest to which the user belongs in each period, using the user's search query, action history, or purchase history as user information.

[0123] In addition, the determination unit 132 determines the regions of interest to which the user belongs in each period, using a model learned to determine whether a user belongs to each region, based on the user information of the users belonging to each region of interest.

[0124] In addition, the specifying unit 133 calculates the average period for which a user belongs to each region, and specifies, as boredom-prone users, those users among the users whose period of belonging to each region is shorter than the average period.

[0125] In addition, the extraction unit 134 extracts features common to the regions of interest within each user or among users, using the vectorized user information.

[0126] In addition, the extraction unit 134 excludes the features of the regions to which fan users who continuously show interest belong, and extracts features common to the regions of interest.

[0127] In addition, the providing unit 136 provides information regarding regions that the boredom-prone users potentially have an interest in, to the boredom-prone users, at the timing when their interest in the currently interesting region has decreased.

[0128] By any one or combination of the above-described processes, the information processing apparatus according to the present application can provide information according to the analysis of people with boredom.

[0129] 〔8. Hardware Configuration〕 In addition, the terminal device 10 and the information providing device 100 according to the above-described embodiment are realized by a computer 1000 having a configuration as shown in FIG. 9, for example. Hereinafter, the information providing device 100 will be described as an example. FIG. 9 is a diagram showing an example of a hardware configuration. The computer 1000 has a form in which an output device 1010, an input device 1020 are connected, and an arithmetic device 1030, a primary storage device 1040, a secondary storage device 1050, an output I / F (Interface) 1060, an input I / F 1070, and a network I / F 1080 are connected by a bus 1090.

[0130] The arithmetic device 1030 operates based on programs stored in the primary storage device 1040 and the secondary storage device 1050, programs read from the input device 1020, etc., and executes various processes. The arithmetic device 1030 is realized by, for example, a CPU (Central Processing Unit), an MPU (Micro Processing Unit), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or the like.

[0131] The primary storage device 1040 is a memory device that primarily stores data used by the arithmetic unit 1030 for various operations, such as a RAM (Random Access Memory). Also, the secondary storage device 1050 is a storage device in which data used by the arithmetic unit 1030 for various operations and various databases are registered, and is realized by a ROM (Read Only Memory), HDD (Hard Disk Drive), SSD (Solid State Drive), flash memory, etc. The secondary storage device 1050 may be an internal storage or an external storage. Also, the secondary storage device 1050 may be a removable storage medium such as a USB (Universal Serial Bus) memory or an SD (Secure Digital) memory card. Also, the secondary storage device 1050 may be a cloud storage (online storage), NAS (Network Attached Storage), file server, etc.

[0132] The output I / F 1060 is an interface for transmitting information to be output to an output device 1010 that outputs various types of information, such as a display, a projector, and a printer. For example, it is realized by a connector of a standard such as USB (Universal Serial Bus), DVI (Digital Visual Interface), or HDMI (registered trademark) (High Definition Multimedia Interface). Also, the input I / F 1070 is an interface for receiving information from various input devices 1020 such as a mouse, a keyboard, a keypad, a button, and a scanner, and is realized by, for example, USB or the like.

[0133] Also, the output I / F 1060 and the input I / F 1070 may be wirelessly connected to the output device 1010 and the input device 1020, respectively. That is, the output device 1010 and the input device 1020 may be wireless devices.

[0134] Further, the output device 1010 and the input device 1020 may be integrated like a touch panel. In this case, the output I / F 1060 and the input I / F 1070 may also be integrated as an input / output I / F.

[0135] Note that the input device 1020 may be a device that reads information from an optical recording medium such as a CD (Compact Disc), a DVD (Digital Versatile Disc), a PD (Phase change rewritable Disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory.

[0136] The network I / F 1080 receives data from other devices via the network N and sends it to the arithmetic unit 1030, and also sends the data generated by the arithmetic unit 1030 via the network N to other devices.

[0137] The arithmetic unit 1030 controls the output device 1010 and the input device 1020 via the output I / F 1060 and the input I / F 1070. For example, the arithmetic unit 1030 loads a program from the input device 1020 or the secondary storage device 1050 onto the primary storage device 1040 and executes the loaded program.

[0138] For example, when the computer 1000 functions as the information providing device 100, the arithmetic unit 1030 of the computer 1000 realizes the function of the control unit 130 by executing the program loaded onto the primary storage device 1040. Further, the arithmetic unit 1030 of the computer 1000 may load a program acquired from other devices via the network I / F 1080 onto the primary storage device 1040 and execute the loaded program. Further, the arithmetic unit 1030 of the computer 1000 may cooperate with other devices via the network I / F 1080 and call and use the functions and data of the program from other programs of other devices.

[0139] [9. Others] The embodiments of the present application have been described above, but the present invention is not limited by the contents of these embodiments. Further, the above-described components include those that can be easily assumed by those skilled in the art, those that are substantially the same, and those within the so-called equivalent range. Furthermore, the above-described components can be combined as appropriate. Still further, various omissions, substitutions, or changes of the components can be made without departing from the gist of the above-described embodiments.

[0140] Also, among the respective processes described in the above embodiments, all or part of the processes described as being automatically performed can be manually performed, or all or part of the processes described as being manually performed can be automatically performed by a known method. In addition, regarding the processing procedures, specific names, and information including various data and parameters shown in the above documents and drawings, they can be arbitrarily changed unless otherwise specified. For example, the various information shown in each figure is not limited to the illustrated information.

[0141] Also, each component of each illustrated device is a functional concept, and it is not necessarily physically configured as shown in the figure. That is, the specific form of the distribution and integration of each device is not limited to that shown in the figure, and all or part of it can be functionally or physically distributed and integrated in any unit according to various loads, usage situations, etc.

[0142] For example, the above-described information providing device 100 may be realized by a plurality of server computers, and depending on the function, the configuration can be flexibly changed, such as by calling an external platform or the like using an API (Application Programming Interface) or network computing.

[0143] Also, the above-described embodiments and modification examples can be appropriately combined as long as the processing contents do not conflict.

[0144] In addition, the "section (section, module, unit)" described above can be read as "means", "circuit", etc. For example, the collection section can be read as a collection means or a collection circuit.

Explanation of Signs

[0145] 1 Information processing system 10 Terminal device 100 Information providing device 110 Communication section 120 Storage section 121 User information database 122 History information database 123 Boredom information database 130 Control section 131 Collection section 132 Judgment section 133 Identification section 134 Extraction section 135 Estimation section 136 Provision section 137 Learning section

Claims

1. A collection unit that collects user information; A determination unit that determines, based on the similarity between the user information of the user and the user information of the affiliated users belonging to a specific area of interest, the area of interest to which the user belongs in each period; As the transition of the area, a specifying unit that specifies users with a perishable nature whose area of interest is likely to change among the users, based on the average period for which the user belongs to each area of interest; An extraction unit that extracts features common to the determined area from the user information of the specified perishable users; An estimation unit that estimates, as an area in which the user potentially has an interest, an area that has the common features and has no search history or purchase history in the user information; An information processing apparatus comprising the above.

2. The information processing apparatus according to claim 1, further comprising a provision unit that provides information regarding an area in which the perishable user potentially has an interest to the perishable user.

3. The information processing apparatus according to claim 1 or 2, further comprising a learning unit that learns features common to the area within each user or among users.

4. The determination unit determines the area of interest to which the user belongs in each period, using, as the user information, the search query, behavior history, or purchase history of the user.

5. The information processing apparatus according to any one of claims 1 to 3, characterized in that the determination unit determines the area of interest to which the user belongs in each period, using a model learned to determine whether the user belongs to each area based on the user information of the users belonging to each area of interest.

6. The specifying unit calculates the average period for which the user belongs to each area, and specifies, as the perishable users, users among the users whose period of belonging to each area is shorter than the average period.

7. The information processing apparatus according to any one of claims 1 to 5, characterized in that the extraction unit extracts features common to the area within each user or among users, using the vectorized user information.

8. The extraction unit extracts features common to the area within each user or among users, using the vectorized user information.

9. The information processing apparatus according to any one of claims 1 to 6, characterized in that the extraction unit extracts features common to the area within each user or among users, using the vectorized user information.

10. The extraction unit extracts features common to the area within each user or among users, using the vectorized user information.

11. The information processing apparatus according to any one of claims 1 to 7, characterized in that the extraction unit extracts features common to the area within each user or among users, using the vectorized user information.

12. The extraction unit extracts features common to the area within each user or among users, using the vectorized user information.

13. The information processing apparatus according to any one of claims 1 to 8, characterized in that the extraction unit extracts features common to the area within each user or among users, using the vectorized user information.

14. The extraction unit excludes the characteristics of the area to which the fan users who continuously show interest belong, and extracts the characteristics common to the areas. The information processing apparatus according to any one of claims 1 to 7, characterized in that.

9. The providing unit provides information about an area in which the saturation user potentially has an interest at a timing when the interest in the area in which the saturation user is currently interested has decreased. The information processing apparatus according to claim 2, characterized in that.

10. An information processing method executed by an information processing apparatus, A collection step of collecting user information, A determination step of determining the area of interest to which the user belongs in each period based on the similarity between the user information of the user and the user information of the affiliated users belonging to a specific area of interest, As the transition of the area, a specifying step of specifying a saturation user whose area of interest is likely to change among the users based on the average period for which the user belongs to each area of interest, An extraction step of extracting the characteristics common to the determined areas from the user information of the specified saturation user, An estimation step of estimating, as an area in which the user potentially has an interest, an area that has the common characteristics and has no search history or purchase history in the user information. An information processing method characterized by including.

11. A collection procedure for collecting user information, A determination procedure for determining the area of interest to which the user belongs in each period based on the similarity between the user information of the user and the user information of the affiliated users belonging to a specific area of interest, As the transition of the area, a specifying procedure for specifying a saturation user whose area of interest is likely to change among the users based on the average period for which the user belongs to each area of interest, An extraction procedure for extracting the characteristics common to the determined areas from the user information of the specified saturation user, An estimation procedure for estimating, as an area in which the user potentially has an interest, an area that has the common characteristics and has no search history or purchase history in the user information. An information processing program for causing a computer to execute.

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