JP7682064B2 - Information processing device, information processing method, and information processing program - Google Patents

Description

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

A technology has been disclosed that can recommend other services even if they do not share common information (such as user history).

JP 2012-150561 A

However, the above conventional technology merely correlates the metadata attached to the items handled by each service and presents highly relevant items to the user. Although providing information about areas of interest or related matters to users is commonly used, there is room for improvement in the way information is provided to users. For example, no information is provided based on whether or not the user tends to get bored easily.

This application was made in consideration of the above, and aims to provide information based on an analysis of people who get bored easily.

The information processing device according to the present application is characterized by comprising a collection unit that collects user information, a determination unit that determines the area of interest to which the user belongs for each period based on the user information, a determination unit that identifies users who tend to get bored and whose interests are likely to change based on the transition of the area, and an extraction unit that extracts features of the area in which the user who tends to get bored is likely to be interested.

According to one aspect of the embodiment, information can be provided based on an analysis of people who tend to get bored easily.

FIG. 1 is an explanatory diagram showing an overview of an information processing method according to an embodiment. FIG. 2 is a diagram illustrating an example of the configuration of an information processing system according to the embodiment. FIG. 3 is a diagram illustrating an example of the configuration of a terminal device according to the embodiment. FIG. 4 is a diagram illustrating an example of the configuration of an information providing device according to an embodiment. FIG. 5 is a diagram illustrating an example of the user information database. FIG. 6 is a diagram illustrating an example of the history information database. FIG. 7 is a diagram showing an example of the boredom information database. FIG. 8 is a flowchart showing a processing procedure according to the embodiment. FIG. 9 is a diagram illustrating an example of a hardware configuration.

Below, the information processing device, 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 device, information processing method, and information processing program according to the present application are not limited to these embodiments. In addition, the same components in the following embodiments will be denoted by the same reference numerals, and duplicated descriptions will be omitted.

[1. Overview of information processing method]
First, an overview of an information processing method performed by an information processing device according to an embodiment will be described with reference to Fig. 1. Fig. 1 is an explanatory diagram showing an overview of an information processing method according to an embodiment. Note that Fig. 1 will be described by taking as an example a case where information is provided in response to an analysis of a person who easily gets bored.

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 via a network N (see FIG. 2) in a wired or wireless manner so as to be able to communicate with each other. In this embodiment, the terminal device 10 cooperates with the information providing device 100.

The terminal device 10 is a smart device such as a smartphone or tablet used by a user U, and is a mobile terminal device capable of communicating with any server device via a wireless communication network such as 4G (Generation) or LTE (Long Term Evolution). The terminal device 10 has a screen such as a liquid crystal display with touch panel functionality, and accepts various operations on displayed data such as content, such as tapping, sliding, scrolling, etc., performed by the user U with a finger or a stylus. An operation performed on an area of the screen where content is displayed may be considered to be an operation on the content. 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.

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

The information providing device 100 may also be an information processing device that provides some kind of web service online to the terminal device 10 of each user U. For example, the information providing device 100 may provide services such as Internet connection, search services, SNS (Social Networking Service), electronic commerce (EC), electronic payment, online games, online banking, online trading, accommodation and ticket reservations, video and music distribution, news, maps, route searches, route guidance, line information, operation information, and weather forecasts as web services. In practice, the information providing device 100 may cooperate with various servers that provide the above-mentioned web services and act as an intermediary for the web services or be responsible for processing the web services.

The information providing device 100 can acquire user information about the user U. For example, the information providing device 100 acquires information about the attributes of the user U, such as the gender, age, and residential area of the user U. The information providing device 100 then stores and manages the information about the attributes of the user U together with identification information (such as a user ID) indicating the user U.

The information providing device 100 also acquires various history information (log data) indicating the behavior of the user U from the terminal device 10 of the user U or from various servers based on the user ID, etc. For example, the information providing device 100 acquires a location history, which is a history of the location and date and time of the user U, from the terminal device 10. The information providing device 100 also acquires a search history, which is a history of search queries entered by the user U, from a search server (search engine). The information providing device 100 also acquires a browsing history, which is a history of content viewed by the user U, from a content server. The information providing device 100 also acquires a purchase history (payment history), which is a history of product purchases and payment processing by the user U, from an electronic commerce server or a payment processing server. The information providing device 100 may also acquire a listing history and a sales history, which are a history of listings on the marketplace by the user U, from an electronic commerce server or a payment server. The information providing device 100 also acquires a posting history, which is a history of posts by the user U, from a posting server or an SNS server that provides a word-of-mouth posting service.

The information providing device 100 according to this embodiment provides information based on an analysis of a person (user) who easily gets bored. In this embodiment, the information providing device 100 analyzes the order and timeline of interest shown in a certain area (field, subject, matter) to find an area that the easily bored person can become engrossed in (get addicted to) in the future.

A person who easily gets bored (a user who easily gets bored) is, for example, someone who has an interest or concern in a particular area (field, subject, matter) for a period that is below average, but who searches for it and spends money on it in that period. In other words, they are people who become enthusiastic or crazy about a particular area for a period of time, and gather information and consume intensively, but this does not last long. On the other hand, a fan (a fan user) is someone who continues to be enthusiastic or crazy about a particular area, and gather information and consume intensively.

[1-1. Estimation of users who get bored easily and provision of information]
As shown in Fig. 1, the information providing device 100 collects user information (search query, behavior history, purchase history, etc.) related to the user U via a network N (see Fig. 2) (step S1). For example, the information providing device 100 directly or indirectly collects location information, attribute information, history information, etc. of the user U via the network N (see Fig. 2). In addition, when the information providing device 100 receives a request for a search, e-commerce, etc. from the terminal device 10 of each user U, it may obtain the search query, purchase information, location information, etc., and accumulate them as history information.

Next, the information providing device 100 determines, for each user, the area in which the user has shown interest or concern from the user information (search query, behavior history, purchase history, etc.) (step S2). For this determination, a machine learning-based determination model for each area can be used. For example, the information providing device 100 can learn the characteristics of the user information (search query, behavior history, purchase history, etc.) of users who are certain to belong to each area (appropriately referred to as "belonging users"), and can construct and use a model that determines whether the user is engrossed in a particular area when the user information is input. The information providing device 100 may also determine the area in which the user has shown interest or concern based on rules.

Next, the information providing device 100 identifies a user whose interests are likely to change (a person who easily gets bored) based on the period and the degree to which the user U was interested in each area (step S3). A person who easily gets bored is, for example, a person whose interest does not last in each area (a person who does not continue a particular hobby). For example, the information providing device 100 identifies a user who frequently shifts interest from one area to another (a user who is interested in a particular area for a shorter period than the average) as a person who easily gets bored.

At this time, the information providing device 100 may estimate the average (or median) period of interest (period of addiction) for each area, and identify users U who have an interest period shorter than the estimated average as users whose interests are likely to change (people who get bored easily).

Next, the information providing device 100 extracts the time-series characteristics of the areas in which the easily bored user showed interest based on the user information (step S4). For example, the information providing device 100 extracts the areas in which the easily bored user showed interest each month and the order in which they were interested. The information providing device 100 may also take into account trends (the chronological order of buzz words and areas), etc. The information providing device 100 performs a Markov process analysis on the vector space of Word2vec, for example. Here, Word2vec is merely one example of natural language processing.

At this time, the information providing device 100 may extract time-series features of the areas in which the easily bored user U showed interest, excluding the area features of the user information of the fan users. In other words, by extracting time-series features that are specific to a user who easily gets bored, the information providing device 100 can estimate with higher accuracy areas in which the easily bored user may be interested in the future, and can also provide information about the estimated areas to the easily bored user.

Next, the information providing device 100 estimates areas in which the user U may be interested in the future based on the time-series characteristics of the extracted areas (step S5). For example, based on the time-series characteristics of areas in which a user who gets bored easily in the past has shown interest, that is, a transition in the order of "sports" → "food" → "movies," the information providing device 100 estimates "movies" as an area in which the user U may be interested in the future if the areas in which the user U has shown interest have transitioned in the order of "sports" → "food." At this time, the information providing device 100 may estimate the time when the user U will be interested in the areas in which the user U may be interested in the future.

Then, the information providing device 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 device 100 generates recommendation information about areas that the user U may be interested in in the future (areas that the user U is likely to be interested in next) based on the user information of the user U, and provides the recommendation information. At this time, the information providing device 100 may provide the recommendation information immediately before the estimated time of interest.

The information providing device 100 also learns the time-series characteristics of the areas in which the easily bored user U showed interest (step S7). For example, the information providing device 100 uses a machine learning model to learn the areas in which the easily bored user U showed interest each month and the order in which they showed interest, and based on the learning results, estimates the areas in which the easily bored user may be interested in the future and the timing of such interest.

In this manner, in this embodiment, the information providing device 100 collects user information, determines from the user information the history of areas in which the user has been interested (changes in interests), and identifies users whose interests are likely to change (users who get bored easily). Next, the information providing device 100 extracts chronological characteristics of areas of interest from the user information of users who get bored easily. Then, the information providing device 100 estimates from the extracted characteristics areas in which the user who gets bored easily is likely to be interested in in the future, and provides recommendation information for the user who gets bored easily. Thus, the information providing device 100 can provide information according to an analysis of people who get bored easily.

Here, we will explain the importance of analyzing users who get bored easily. First, users who get bored easily are those who concentrate on certain actions (spending amount, purchases, etc.), but as a higher-level concept, move to another area for a short period of time. In this case, even if the interests of users who get bored easily change, they will move with a certain degree of tendency. Therefore, analyzing this tendency and making effective recommendations to users who get bored easily is of great importance in terms of marketing. For example, it is possible to narrow down the characteristics based on information collected from users to those unique to users who get bored easily, and use them as ideas for collaborations or product planning. Furthermore, the areas that users who get bored easily are interested in may also be areas that many people are interested in, so they can also be used to predict areas that will be popular in the future.

2. Example of information processing system configuration
Next, a configuration of an information processing system 1 including an information providing device 100 according to an embodiment will be described with reference to Fig. 2. Fig. 2 is a diagram showing an example of a configuration of the information processing system 1 according to an embodiment. As shown in Fig. 2, the information processing system 1 according to an embodiment includes a terminal device 10 and an information providing device 100. These various devices are connected to each other via a network N so as to be able to communicate with each other by wire or wirelessly. The network N is, for example, a local area network (LAN) or a wide area network (WAN) such as the Internet.

Furthermore, the number of devices 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 to simplify the illustration, but this is merely an example and is not limiting, and there may be two or more devices.

The terminal device 10 is an information processing device used by a 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 console or AV equipment with a communication function, a car navigation system, a wearable device such as a smart watch or a head-mounted display, smart glasses, etc.

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

The information providing device 100 is, for example, a PC, a server device, a mainframe, a workstation, etc. The information providing device 100 may be realized by cloud computing.

3. Example of terminal device configuration
Next, the configuration of the terminal device 10 will be described with reference to Fig. 3. Fig. 3 is a diagram showing an example of the configuration 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.

(Communication unit 11)
The communication unit 11 is connected to a 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 network interface card (NIC), an antenna, or the like.

(Display section 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 electro-luminescent display (OLED). The display unit 12 is also a touch panel display, but is not limited to this.

(Input unit 13)
The input unit 13 is an input device that accepts various operations from the user U. For example, the input unit 13 has buttons for inputting characters, numbers, and the like. The input unit 13 may be an input/output port (I/O port), a USB (Universal Serial Bus) port, or the like. In addition, when the display unit 12 is a touch panel display, a part of the display unit 12 functions as the input unit 13. In addition, the input unit 13 may be a microphone that accepts voice input from the user U, or the like. The microphone may be wireless.

(Positioning unit 14)
The positioning unit 14 receives signals (radio waves) transmitted from satellites of a GPS (Global Positioning System), and acquires position information (e.g., latitude and longitude) indicating the current position of the terminal device 10 based on the received signals. That is, the positioning unit 14 measures the position of the terminal device 10. Note that the GPS is merely an example of a GNSS (Global Navigation Satellite System).

The positioning unit 14 can also measure the position using various methods other than GPS. For example, the positioning unit 14 may measure the position using various communication functions of the terminal device 10 as an auxiliary positioning means for position correction, etc., as described below.

(Wi-Fi positioning)
For example, the positioning unit 14 uses a Wi-Fi (registered trademark) communication function of the terminal device 10 or a communication network provided by each communication company to measure the position of the terminal device 10. Specifically, the positioning unit 14 performs Wi-Fi communication or the like and measures the distance to a nearby base station or access point to measure the position of the terminal device 10.

(Beacon positioning)
Furthermore, the positioning unit 14 may measure the position by using a 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.

(geomagnetic positioning)
In addition, the positioning unit 14 locates the position of the terminal device 10 based on a geomagnetic pattern of a structure that has been measured in advance and a geomagnetic sensor provided in the terminal device 10 .

(RFID positioning)
Furthermore, for example, if the terminal device 10 has a function of an RFID (Radio Frequency Identification) tag equivalent to a contactless IC card used at station ticket gates, in stores, etc., or has a function of reading an RFID tag, the location of use is recorded together with information on a payment or the like made by the terminal device 10. The positioning unit 14 may obtain such information to measure the location of the terminal device 10. Furthermore, the location may be measured by an optical sensor, an infrared sensor, or the like provided in the terminal device 10.

If necessary, the positioning unit 14 may use one or a combination of the positioning means described above to determine the position of the terminal device 10.

(Sensor unit 20)
The sensor unit 20 includes various sensors mounted on or connected to the terminal device 10. The connection may be wired or wireless. For example, the sensors may be detection devices other than the terminal device 10, such as wearable devices and wireless devices. In the example shown in FIG. 3, the sensor unit 20 includes an acceleration sensor 21, a gyro sensor 22, an air 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.

The above-mentioned sensors 21 to 28 are merely examples and are not limiting. In other words, 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.

The acceleration sensor 21 is, for example, a three-axis acceleration sensor, and detects the physical movement of the terminal device 10, such as the direction of movement, speed, and acceleration of the terminal device 10. The gyro sensor 22 detects the physical movement of the terminal device 10, such as the tilt in three axial directions, based on the angular velocity of the terminal device 10. The air pressure sensor 23 detects, for example, the air pressure around the terminal device 10.

Since the terminal device 10 is equipped with the above-mentioned acceleration sensor 21, gyro sensor 22, air pressure sensor 23, etc., it is possible to determine the position of the terminal device 10 using technology such as Pedestrian Dead-Reckoning (PDR) that utilizes these sensors 21 to 23. This makes it possible to obtain indoor position information that is difficult to obtain using positioning systems such as GPS.

For example, the number of steps, walking speed, and distance walked can be calculated using a pedometer that uses the acceleration sensor 21. In addition, the gyro sensor 22 can be used to know the user U's direction of travel, line of sight, and body inclination. In addition, the air pressure detected by the air pressure sensor 23 can be used to know the altitude and floor on which the user U's terminal device 10 is located.

The air temperature sensor 24 detects, for example, the air temperature around the terminal device 10. The sound sensor 25 detects, for example, 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 around the terminal device 10.

The above-mentioned air 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 air pressure, temperature, sound, and illuminance, and capturing images of the surroundings. In addition, it is possible to improve the accuracy of the location information of the terminal device 10 based on the environment and situation around the terminal device 10.

(Control unit 30)
The control unit 30 includes, for example, a microcomputer having a central processing unit (CPU), a read only memory (ROM), a RAM, an input/output port, and various other circuits. The control unit 30 may also be configured with hardware such as an integrated circuit, for example, an application specific integrated circuit (ASIC) or a field programmable gate array (FPGA). The control unit 30 includes a transmission unit 31, a reception unit 32, and a processing unit 33.

(Transmitter 31)
The transmission unit 31 can transmit, for example, various information input by the user U using the input unit 13, various information detected by each sensor 21 to 28 mounted on or connected to the terminal device 10, and location information of the terminal device 10 measured by the positioning unit 14 to the information providing device 100 via the communication unit 11.

(Receiving unit 32)
The receiving unit 32 can receive various information provided by the information providing device 100 and requests for various information from the information providing device 100 via the communication unit 11 .

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

(Storage unit 40)
The storage unit 40 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 drive (HDD), a solid state drive (SSD), an optical disk, etc. Various programs and various data are stored in the storage unit 40.

4. Example of the configuration of the information providing device
Next, a configuration of the information providing device 100 according to the embodiment will be described with reference to Fig. 4. Fig. 4 is a diagram showing an example of the configuration 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.

(Communication unit 110)
The communication unit 110 is realized by, for example, a network interface card (NIC), etc. The communication unit 110 is also connected to a network N (see FIG. 2) in a wired or wireless manner.

(Storage unit 120)
The storage unit 120 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 HDD, an SSD, an optical disk, etc. As shown in FIG. 4, the storage unit 120 has a user information database 121, a history information database 122, and a boredom information database 123.

(User information database 121)
The user information database 121 stores user information related to the user U. For example, the user information database 121 stores various information such as 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".

"User ID" indicates identification information for identifying user U. Note that "user ID" may be contact information for user U (telephone number, email address, etc.) or may be identification information for identifying user U's terminal device 10.

Furthermore, "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) or information indicating the generation of user U (e.g., 30s). Alternatively, "age" may be information indicating user U's date of birth or information indicating user U's generation (e.g., born in the 1980s). Furthermore, "gender" indicates the gender of user U identified by the user ID.

"Home" indicates the location information of the home of user U, which is identified by the user ID. In the example shown in FIG. 5, "Home" is illustrated as an abstract code such as "LC11", but it may also be latitude and longitude information, etc. For example, "Home" may also be the name of a region or an address.

"Workplace" indicates the location information of the workplace (school in the case of a student) of user U identified by the user ID. In the example shown in FIG. 5, "workplace" is illustrated as an abstract code such as "LC12", but it may also be latitude and longitude information, etc. For example, "workplace" may also be the name of a region or an address.

Furthermore, "interests" indicate the interests of user U identified by the user ID. In other words, "interests" indicate subjects in which user U identified by the user ID is highly interested. For example, "interests" may be search queries (keywords) entered by user U into a search engine. Note that, although one "interest" is illustrated for each user U in the example shown in FIG. 5, there may be multiple "interests."

For example, in the example shown in FIG. 5, the age of user U identified by user ID "U1" is "20s" and the gender is "male." Also, for example, user U identified by user ID "U1" indicates that his home address is "LC11." Also, for example, user U identified by user ID "U1" indicates that his workplace is "LC12." Also, for example, user U identified by user ID "U1" indicates that he is interested in "sports."

In the example shown in FIG. 5, abstract values such as "U1", "LC11", and "LC12" are used to illustrate the data, but it is assumed that specific information such as character strings and numerical values is stored in "U1", "LC11", and "LC12". In the following figures relating to other information, abstract values may also be illustrated.

The user information database 121 may store various information according to the purpose, not limited to the above. For example, the user information database 121 may store various information related to the terminal device 10 of the user U. The user information database 121 may also store information related to the attributes of the user U, such as demographic attributes, psychographic attributes, geographic attributes, and behavioral attributes. For example, the user information database 121 may store information such as name, family structure, place of origin (hometown), occupation, job title, income, qualifications, type of residence (detached house, apartment, etc.), whether or not the user has a car, commuting time, commuting route, commuter pass section (station, line, etc.), frequently used station (other than the nearest station to home or workplace), extracurricular activities (location, time zone, etc.), hobbies, interests, and lifestyle.

(History information database 122)
The history information database 122 stores various information related to history information (log data) indicating the behavior 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".

"User ID" refers to identification information for identifying user U. "Location history" refers to location history, which is a history of user U's location and movements. "Search history" refers to search history, which is a history of search queries entered by user U. "Browse history" refers to browse history, which is a history of content viewed by user U. "Purchase history" refers to purchase history, which is a history of purchases made by user U. "Post history" refers to posting history, which is a history of posts made by user U. "Post history" may include questions about user U's possessions.

For example, in the example shown in FIG. 6, user U identified by user ID "U1" moves as shown in "location history #1," searches as shown in "search history #1," views content as shown in "browsing history #1," purchases specific products at specific stores as shown in "purchase history #1," and posts as shown in "posting history."

In the example shown in FIG. 6, abstract values such as "U1", "Location History #1", "Search History #1", "Browse History #1", "Purchase History #1", and "Post 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", "Browse History #1", "Purchase History #1", and "Post History #1".

The history information database 122 may store various information depending on the purpose, not limited to the above. For example, the history information database 122 may store the usage history of a specific service by the user U. The history information database 122 may also store the history of the user U's visit to a physical store or the history of the user U's visit to a facility. The history information database 122 may also store the payment history of payments (electronic payments) made using the user U's terminal device 10.

(Boredom Information Database 123)
The boredom information database 123 stores various information regarding whether the user U is a person who easily gets 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", "interest level", "interest period", and "boredom".

"User ID" indicates identification information for identifying user U. "Area" indicates the area in which user U was interested. "Category" indicates the category (classification, genre) of the area in which user U was interested. "Level of interest" indicates the degree of interest user U had in the area. "Interest period" indicates the period during which user U was interested in the area. "Tiredness" indicates whether user U is a person who easily gets bored (◯) or not (X) based on the above items. Note that in reality, "tiringness" may be expressed as a level of boredom expressed as a number (1, 2, 3, ...) or a magnitude (large, medium, small, high, medium, low, etc.).

For example, in the example shown in FIG. 7, user U, identified by user ID "U1," has had an interest in "areas #1A-#1E," which belong to "categories #1A-#1E," the level of interest is "interest level #1A-#1E," and the period during which he was interested is "interest period #1A-#1E," which indicates that he is presumed to be "a person who easily gets bored."

In the example shown in FIG. 7, abstract values such as "U1", "Area #1A-#1E", "Category #1A-#1E", "Interest Level #1A-#1E", and "Interest Period #1A-#1E" are used for illustration, but specific information such as character strings and numerical values is stored in "U1", "Area #1A-#1E", "Category #1A-#1E", "Interest Level #1A-#1E", and "Interest Period #1A-#1E".

The boredom information database 123 may store various 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. The boredom information database 123 may also store information regarding similarities or differences between user U and user information of a user whose interests are unlikely to change (a person who is not prone to boredom).

(Control unit 130)
Returning to Fig. 4, the description will be continued. The control unit 130 is a controller, and is realized by, for example, a central processing unit (CPU), a micro processing unit (MPU), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or the like, executing various programs (corresponding to an example of an information processing program) stored in a storage device inside the information providing device 100 using a storage area such as a RAM as a working area. In the example shown in Fig. 4, the control unit 130 has a collection unit 131, a determination unit 132, an identification unit 133, an extraction unit 134, an estimation unit 135, a provision unit 136, and a learning unit 137.

(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 into 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 keywords input by the user U into a search box of a search engine, site, or app via the communication unit 110.

The collection unit 131 also acquires user information about user U via the communication unit 110. For example, the collection unit 131 acquires identification information (such as a user ID) indicating user U, location information of user U, attribute information of user U, etc. from the terminal device 10 of user U. The collection unit 131 may also acquire identification information indicating user U, attribute information of user U, etc. when user U is registered. Then, the collection unit 131 registers the user information in the user information database 121 of the storage unit 120.

The collection unit 131 also acquires various types of history information (log data) indicating the behavior of the user U via the communication unit 110. For example, the collection unit 131 acquires various types of history information indicating the behavior 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 types of history information in the history information database 122 of the storage unit 120.

In this way, the collection unit 131 collects user information (search queries, behavioral history, purchase history, etc.) for each user via the communication unit 110.

(Determination unit 132)
The determination unit 132 determines the area of interest to which the user U belongs in each period based on the user information. For example, the determination unit 132 determines the area of interest to which the user U belongs in each period by using the search query, behavior history, or purchase history of the user U as the user information.

The determination unit 132 also determines the area of interest to which user U belongs in each period, using a model trained to determine whether a user belongs to each area based on the user information of the user who belongs to each area of interest. That is, the determination unit 132 determines the area of interest to which user U belongs in each period, using a model such as a deep neural network (DNN) that has been trained using user information of users (belonging users) who are certain to belong to each area of interest as learning data. At this time, the information providing device 100 may determine whether 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 user.

Here, when determining the similarity, the information providing device 100 may calculate a rule-based score indicating the similarity, and if the calculated similarity exceeds a predetermined threshold, determine whether or not the user U belongs to a specific area.

(Specific part 133)
The identification unit 133 identifies users who are prone to change interests and get bored easily, based on the transition of the areas of interest to which the users belong in each period. For example, the identification unit 133 calculates an average period during which the users belong to each area of interest, and identifies users who belong to each area for a shorter period than the average period as users who get bored easily.

Here, the average period calculated by the identification unit 133 is the average value of the affiliation periods of each area determined by the determination unit 132. Furthermore, the identification unit 133 may identify user U as a user who easily gets bored when all of the affiliation periods of the areas determined to which user U belongs are shorter than the average period, or may identify user U as a user who easily gets bored when a predetermined number of affiliation periods are shorter than the average period, or may identify a user who easily gets bored by comparing with the average period of all the set areas. Furthermore, the identification unit 133 may identify a user who has spent a predetermined amount of money within a certain period of time among the users who satisfy the above-mentioned affiliation period conditions as a user who easily gets bored.

Then, the identification unit 133 registers various types of boredom information in the boredom information database 123 in the storage unit 120.

(Extraction unit 134)
The extraction unit 134 extracts the characteristics of the area in which the user who easily gets bored is likely to be interested. For example, the extraction unit 134 extracts time-series characteristics related to the subject of interest as the characteristics of the area in which the user who easily gets bored is likely to be interested. To explain using the example shown in FIG. 7, the extraction unit 134 extracts the transition of the area of interest in the order of "area #1A" → "area #1B" → "area #1C" → "area #1D" → "area #1E" from the information of the user whose user ID is "U1" registered in the boredom information database 123 of the storage unit 120.

The extraction unit 134 also uses the vectorized user information to extract features of areas in which users who get bored easily are likely to be interested. For example, the extraction unit 134 performs natural language processing using Word2vec and uses vectorized user information to extract features of areas in which users who get bored easily are likely to be interested.

The extraction unit 134 also excludes characteristics of areas to which fan users who have continued to show interest belong, and extracts characteristics of areas in which users who easily get bored are likely to show interest. For example, the extraction unit 134 excludes characteristics from specific campaigns or events as characteristics that are also seen by fan users and other users, and extracts characteristics specific to users who easily get bored.

(Estimation unit 135)
The estimation unit 135 estimates an area in which the easily bored user will be interested in the future. The estimation unit 135 may also estimate a specific future interest of the easily bored user. For example, the estimation unit 135 may estimate "books" as an area in which the easily bored user will be interested in the future, and "mystery novels" as a specific future interest.

Furthermore, the estimation unit 135 estimates the time in the future when the easily bored user will show interest. For example, the estimation unit 135 may estimate that the time when user U will show interest in "books" is "one month from now."

In addition, the estimation unit 135 may use a trained model trained by the learning unit 137 to estimate areas in which a user who easily gets bored is likely to be interested, specific objects of interest, and times when they will be interested.

(Providing unit 136)
The providing unit 136 provides the user who easily gets bored with information on areas that the user will be interested in in the future. For example, the providing unit 136 provides the user who easily gets bored with recommendation information on areas that the user will be interested in in the future or specific subjects of interest.

The providing unit 136 also provides the easily bored user with information about areas that the user will be interested in in the future a predetermined period of time before the time when the user will be interested in the easily bored user. For example, the providing unit 136 provides the easily bored user with recommendation information one week before the estimated time. The providing unit 136 may also provide information about areas that the user will be interested in in the future and the time when the user will be interested to fan users and other users.

(Learning Unit 137)
The learning unit 137 learns the features of areas in which users who get bored easily tend to be interested. For example, when the learning unit 137 receives user information of a user who gets bored easily, the learning unit 137 learns using a model such as DNN so as to output the features of areas in which users who get bored easily tend to be interested. At this time, the learning unit 137 may perform learning by backpropagation or the like.

5. Processing Procedure
Next, a processing procedure by the information providing device 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 processing procedure shown below is repeatedly executed by the control unit 130 of the information providing device 100. Also, the processing procedures below can be executed in a different order. Also, some of the processing procedures below may be omitted.

As shown in FIG. 8, the collection unit 131 of the information providing device 100 collects user information (search queries, behavioral history, purchase history, etc.) for each user via the communication unit 110 (step S101).

Next, the determination unit 132 of the information providing device 100 sets the areas in which the user to be determined has shown interest or concern (step S102).

Next, the determination unit 132 of the information providing device 100 determines, for each user, the areas in which the user has shown interest or concern from the user information (search query, behavioral history, purchase history, etc.) (step S103).

Next, the identification unit 133 of the information providing device 100 identifies users whose interests are likely to change (users who get bored easily) based on the results of the determination of the duration and level of interest (step S104).

Next, the extraction unit 134 of the information providing device 100 extracts time-series characteristics of the areas of interest from the user information of the user who is easily bored (step S105).

Next, the estimation unit 135 of the information providing device 100 estimates the areas in which the easily bored user will be interested in the future and the timing of such interest (step S106).

Next, the providing unit 136 of the information providing device 100 generates recommendation information for the user who tends to get bored easily, and provides information to the user who tends to get bored easily (step S107).

Next, the learning unit 137 of the information providing device 100 learns the time-series characteristics of the areas in which the easily bored user showed interest (step S108).

6. Modifications
The terminal device 10 and the information providing device 100 described above may be implemented in various different forms other than the above embodiment. Therefore, modifications of the embodiment will be described below.

In the above embodiment, some or all of the processing performed by the information providing device 100 may actually be performed by the terminal device 10. For example, the processing may be completed in a stand-alone manner (by the terminal device 10 alone). In this case, the terminal device 10 is assumed to have the functions of the information providing device 100 in the above embodiment. Also, in the above embodiment, since the terminal device 10 is linked to the information providing device 100, from the perspective of the user U, it appears that the processing of the information providing device 100 is also being performed by the terminal device 10. In other words, from another perspective, it can also be said that the terminal device 10 is equipped with the information providing device 100.

In addition, in the above embodiment, the areas that user U is interested in may be famous people, works (literature, art, entertainment, etc.), sports, games, food and drink, stores, facilities, vehicles, countries and regions, etc.

In the above embodiment, the area of interest of user U is not limited to hobbies, but may be work, subjects, lessons, etc. It may also be a company, school, cram school, etc.

In the above embodiment, the areas that the user U is interested in may be matters related to beauty, health, and diet. They may also be matters related to illness, medicine, hospitals, etc.

7. Effects
As described above, the information processing device (terminal device 10 and information providing device 100) according to the present application includes a collection unit 131 that collects user information, a determination unit 132 that determines the area of interest to which the user belongs in each period based on the user information, an identification unit 133 that identifies users who tend to get bored and whose interests are likely to change based on changes in the area, and an extraction unit 134 that extracts characteristics of areas that users who tend to get bored are likely to be interested in.

In addition, the extraction unit 134 extracts time-series features related to the subject of interest as features of areas in which the easily bored user is likely to be interested. In addition, the information processing device according to the present application further includes an estimation unit 135 that estimates areas in which the easily bored user will be interested in the future, and a provision unit 136 that provides the easily bored user with information related to areas in which the easily bored user will be interested in the future.

The information processing device according to the present application further includes a learning unit 137 that learns the characteristics of areas in which a user who easily gets bored is likely to be interested. The estimation unit 135 estimates areas in which the user who easily gets bored will be interested in the future based on the learning results.

The determination unit 132 also uses the user U's search query, behavioral history, or purchase history as user information to determine the area of interest to which the user belongs during each period.

The determination unit 132 also determines the area of interest to which the user U belongs during each period, using a model that has been trained to determine whether or not a user belongs to each area, based on the user information of the user who belongs to each area of interest.

In addition, the identification unit 133 calculates the average period during which a user belongs to each area of interest, and identifies users who belong to each area for a shorter period than the average period as users who tend to get bored easily.

In addition, the extraction unit 134 uses the vectorized user information to extract the characteristics of areas that are likely to interest users who tend to get bored easily.

In addition, the extraction unit 134 excludes the characteristics of areas to which fan users who have continued to show interest belong, and extracts the characteristics of areas in which the easily bored users are likely to show interest.

In addition, the estimation unit 135 further estimates a time when the easily bored user will show interest in the future, and the provision unit 136 provides the easily bored user with information about the area in which he or she will show interest in the future a predetermined period of time before the estimated time.

By using any one or a combination of the above-mentioned processes, the information processing device according to the present application can provide information according to an analysis of people who tend to get bored easily.

8. Hardware Configuration
Moreover, 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. The information providing device 100 will be described below as an example. Fig. 9 is a diagram showing an example of a hardware configuration. The computer 1000 is connected to an output device 1010 and an input device 1020, and has a configuration in which a calculation 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.

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, and the like, and executes various processes. The arithmetic device 1030 is realized, for example, 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.

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

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, projector, printer, etc., and is realized by a connector conforming to a standard such as USB (Universal Serial Bus), DVI (Digital Visual Interface), or HDMI (registered trademark) (High Definition Multimedia Interface). The input I/F 1070 is an interface for receiving information from various input devices 1020, such as a mouse, keyboard, keypad, button, scanner, etc., and is realized by a USB, etc.

In addition, 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.

The output device 1010 and the input device 1020 may be integrated together, such as a touch panel. In this case, the output I/F 1060 and the input I/F 1070 may also be integrated together as an input/output I/F.

The input device 1020 may be a device that reads information from, for example, an optical recording medium such as a CD (Compact Disc), a DVD (Digital Versatile Disc), or 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.

The network I/F 1080 receives data from other devices via the network N and sends it to the computing device 1030, and also transmits data generated by the computing device 1030 to other devices via the network N.

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.

For example, when the computer 1000 functions as the information providing device 100, the arithmetic unit 1030 of the computer 1000 executes a program loaded onto the primary storage device 1040 to realize the functions of the control unit 130. The arithmetic unit 1030 of the computer 1000 may also load a program acquired from another device via the network I/F 1080 onto the primary storage device 1040 and execute the loaded program. The arithmetic unit 1030 of the computer 1000 may also cooperate with other devices via the network I/F 1080 and use the functions and data of a program by calling them from other programs of the other devices.

[9. Other]
Although the embodiments of the present application have been described above, the present invention is not limited to the contents of these embodiments. 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 appropriately combined. 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 in the drawings.

In addition, each component of each device shown in the figure is a functional concept, and does not necessarily have to be physically configured as shown in the figure. In other words, the specific form of distribution and integration of each device is not limited to that shown in the figure, 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 100 described above may be realized by multiple server computers, and depending on the functions, the configuration can be flexibly changed, such as by calling an external platform using an API (Application Programming Interface) or network computing.

The above-described embodiments and variations can be combined as appropriate to the extent that they do not cause inconsistencies in the processing content.

The above-mentioned "section, module, unit" can be read as "means" or "circuit." For example, the collection unit can be read as collection means or collection circuit.

REFERENCE SIGNS LIST 1 Information processing system 10 Terminal device 100 Information providing device 110 Communication unit 120 Storage unit 121 User information database 122 History information database 123 Boredom information database 130 Control unit 131 Collection unit 132 Determination unit 133 Identification unit 134 Extraction unit 135 Estimation unit 136 Provision unit 137 Learning unit

Claims (11)

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 a similarity between the user information of the user and the user information of a user who belongs to a specific area of interest;
an identification unit that identifies , as a transition of the area, a user who is easily bored and whose interests are likely to change, among the users, based on an average period during which the user belongs to each area of interest ;
an extraction unit that extracts time-series features related to an object of interest as features of an area in which the easily bored user is likely to show interest based on the collected user information;
An estimation unit that estimates an area in which the identified user who easily gets bored will be interested in in the future based on the user information of the user who easily gets bored in the past;
An information processing device comprising: a provision unit for providing the easily bored user with information on areas that the user will be interested in in the future ;
The information processing apparatus according to claim 1 , further comprising: A learning unit that learns features of an area in which the easily bored user is likely to be interested ;
The information processing apparatus according to claim 2 , further comprising : The information processing device 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 by using the user's search query, behavioral history, or purchase history as the user information. The information processing device of any one of claims 1 to 4, characterized in that the determination unit determines the area of interest to which the user belongs in each period using a model trained to determine whether or not a user belongs to each area based on user information of the user who belongs to each area of interest. The information processing device according to any one of claims 1 to 5, characterized in that the identification unit calculates an average period during which the user belongs to each area of interest, and identifies the user who belongs to each area for a shorter period than the average period as the user who is prone to boredom. The information processing device according to any one of claims 1 to 6, wherein the extraction unit extracts features of an area in which the user who easily gets bored is likely to be interested, by using the vectorized user information. The information processing device according to any one of claims 1 to 7, characterized in that the extraction unit excludes characteristics of areas to which fan users who have shown continuous interest belong, and extracts characteristics of areas to which the user who easily gets bored is likely to show interest. The estimation unit further estimates a time when the easily bored user will show interest in the future,
The information processing device according to claim 2 , wherein the providing unit provides the easily bored user with information on an area that the user will be interested in in the future a predetermined period before the time. An information processing method executed by an information processing device,
A collection step of collecting user information;
A determination step of determining an area of interest to which the user belongs in each period based on a similarity between the user information of the user and the user information of a user who belongs to a particular area of interest;
A step of identifying users who tend to have their interests easily changed and who tend to get bored, based on an average period during which the users belong to each of the areas of interest as a transition of the areas;
an extraction step of extracting time-series features of an interest as features of an area in which the easily bored user is likely to be interested based on the collected user information;
an estimation step of estimating an area in which the identified user who is prone to boredom will be interested in in the future based on the user information of the user who is prone to boredom in the past;
13. An information processing method comprising: The collection procedures for collecting User Information;
A determination step of determining an area of interest to which the user belongs in each period based on a similarity between the user information of the user and the user information of a user belonging to a particular area of interest;
a step of identifying users who tend to have their interests easily changed and get bored easily, based on an average period during which the users belong to each of the interest areas as a transition of the interest areas;
an extraction step of extracting time-series features of an interest as a feature of an area in which the easily bored user is likely to be interested based on the collected user information;
an estimation step of estimating an area in which the identified user who is prone to boredom will be interested in in the future based on the user information of the user who is prone to boredom in the past;
An information processing program for causing a computer to execute the above.

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