JP2022139297A - Information processing device, information processing method, and information processing program - Google Patents

Abstract

To provide beneficial information in a higher-speed manner.SOLUTION: An information processing device comprises: an estimation section which, based on search information pertaining to a search history of a user using an information provision service providing the search information pertaining to keywords searched by a large number of unspecified searchers, estimates, for each keyword to be searched, a filtering condition for the user to filter search results pertaining to the keywords to be searched; a storage section which preliminarily stores the search results filtered by the filtering condition estimated by the estimation section; and a display section which displays the search results preliminarily stored in the storage section if a display request of the search results filtered by the filtering condition estimated by the estimation section is accepted from the user.SELECTED DRAWING: Figure 1

Description

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

2. Description of the Related Art In recent years, with the rapid spread of the Internet, techniques related to analysis using various information on the Internet, for example, have been provided. For example, there has been proposed a technique for extracting information on needs for a target provided by a predetermined business operator based on a search query input by a user.

JP 2019-32776 A

However, it is not always possible to obtain useful information with the above-described conventional techniques. For example, in the above-described prior art, since the information on the needs for the target provided by the predetermined business operator is only extracted, the information on the latent needs for the target provided by the predetermined business operator can be visually and appropriately displayed. cannot be presented to

The present application has been made in view of the above, and an object thereof is to provide an information processing apparatus, an information processing method, and an information processing program capable of providing useful information at a higher speed.

The information processing apparatus according to the present application provides search information related to keywords searched by an unspecified number of searchers based on search information related to the search history of the user who uses the information providing service. an estimating unit for estimating, for each of the search target keywords, a narrowing condition for narrowing search results related to a keyword; a storage unit for pre-storing the search results narrowed down by the narrowing condition estimated by the estimating unit; and the estimating unit. a display unit for displaying the search results stored in advance in the storage unit when a display request for the search results narrowed down by the narrowing conditions estimated by the user is received from the user. and

According to one aspect of the embodiment, there is an effect that useful information can be provided at a higher speed.

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

Embodiments for implementing an information processing apparatus, an information processing method, and an information processing program according to the present application (hereinafter referred to as "embodiments") will be described in detail below with reference to the drawings. The information processing apparatus, information processing method, and information processing program according to the present application are not limited to this embodiment. Also, in the following embodiments, the same parts are denoted by the same reference numerals, and overlapping descriptions are omitted.

In addition, the "user U" in the following embodiments refers to a user who uses an information providing service DS1 that provides search information related to keywords searched by an unspecified number of users (also referred to as an unspecified number of searchers). It refers to a user who is a person (also called a user of the information providing service DS1) and an unspecified number of users.

[1. Outline of information processing method]
First, with reference to FIG. 1, an overview of the information processing method performed by the information providing apparatus according to the embodiment will be described. FIG. 1 is an explanatory diagram showing an outline of an information processing method according to an embodiment. In FIG. 1, the case where the information providing apparatus 100 improves the display speed of a keyword map related to a search target keyword received from the user U will be described as an example.

Here, the keyword map is briefly explained. A keyword map related to a search target keyword is information that visualizes the co-occurrence relationship between co-occurrence keywords searched together with the search target keyword. For example, the keyword map KM1 shown on the left side of FIG. 1 is searched together with the keyword "cosmetics company #1" when the keyword to be searched is the name of the cosmetics company "cosmetics company #1". This is a visualization of co-occurrence relationships between co-occurrence keywords. Specifically, the co-occurring keywords “product name A”, “product name B”, “ingredients”, “release date”, “skin lotion”, “rough skin” that are searched together with “cosmetics company # 1” ”, “moisturizing”, and “price” are indicated by circular nodes. The size of a node indicates the search volume (the number of searches) of co-occurring keywords. Links connecting nodes indicate co-occurrence relationships between co-occurring keywords.

Prior to the description of FIG. 1, the information providing service DS1 provided by the information providing apparatus 100 will be described. The information providing service DS1 provides search information (for example, a keyword map) related to searched keywords to an unspecified number of users. In other words, the information providing service DS1 provides analysis tools for search queries. Due to the nature of such services, users of the information providing service DS1 use the service differently from users of general search services. Specifically, users of the information providing service DS1 often use the information providing service DS1 for the purpose of analyzing trends in the industry or market to which they belong.

For example, a user who works in the media industry uses the information provision service DS1 to perform a search that narrows down the target group targeted by the media (for example, women in their twenties for young women). Analyze the keyword map for your target keyword. Also, for example, a user who works in the advertising industry uses the information providing service DS1 to create a keyword map of search target keywords narrowed down with narrowing conditions related to the period for which trend analysis is desired (for example, the past week). Analyze serial change. In this way, the user of the information providing service DS1 uses the information providing service DS1 to analyze the search trend of the search target keyword for the purpose of analyzing trends in the industry or market to which the user belongs.

Thus, the information providing service DS1 is characterized by being a search query analysis tool. Therefore, the information providing apparatus 100 that provides the information providing service DS1 not only receives requests from users to display search results (for example, keyword maps) related to search target keywords, but also receives various analysis axes that users desire to analyze. A request to display search results obtained by narrowing search results related to a keyword to be searched by a narrowing condition corresponding to is accepted. In addition, the narrowing down conditions that the information providing apparatus 100 accepts from users show a certain tendency for each industry to which the users belong. Furthermore, in the narrowing down conditions that the information providing apparatus 100 accepts from users, a certain tendency can be seen for each user based on the user's past search history in the information providing service DS1.

In general, cache servers store search results requested by users. Thereby, when the cache server receives a display request for the same search result again from the user, it displays the search result saved in advance, thereby enabling the search result to be displayed at high speed. On the other hand, the information providing apparatus 100 often receives a request from the user to display search results narrowed down by a narrowing-down condition corresponding to an analysis axis on which the user desires analysis together with a search target keyword. Therefore, the information providing apparatus 100 estimates narrowing conditions to be stored in the cache for each keyword to be searched based on the user's past search history in the information providing service DS1. Then, the information providing apparatus 100 generates search results narrowed down by the estimated narrowing-down conditions that should be held in the cache, and caches the generated narrowed down search results. Furthermore, the information providing apparatus 100 estimates the priority of the narrowing-down conditions to be held in the cache based on the user's past search history in the information providing service DS1, and the search results narrowed down by the narrowing-down conditions with high priority are calculated. are preferentially cached.

As a result, since the information providing apparatus 100 generates and stores the narrowed down search results in advance, the narrowed down search results are generated and displayed after receiving the narrowing conditions. Search results can be displayed at high speed. Therefore, the information providing device 100 can provide useful information at a higher speed.

From here, it returns to description of FIG. 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 communicably connected to each other by wire or wirelessly via a network N (see FIG. 2).

The terminal device 10 is a smart device such as a smartphone or tablet used by the user U, and communicates with an arbitrary server device via a wireless communication network such as 4G (Generation) or LTE (Long Term Evolution). It is a portable terminal device that can The terminal device 10 has a screen such as a liquid crystal display, which has a touch panel function. Accepts various operations. An operation performed on an area where content is displayed on the screen may be an operation on the content. In addition, the terminal device 10 may be an information processing device such as a desktop PC (Personal Computer) or a notebook PC as well as a smart device.

In this embodiment, the terminal device 10 cooperates with the information providing device 100, and on the screen for map mapping of co-occurring keywords provided by the information providing service DS1, the category is classified based on the keyword input by the user U. Display a keyword map showing the co-occurrence network of A keyword map that shows a co-occurrence network of categories is a map of co-occurring keywords that is mapped so that co-occurrence relationships between keywords can be visually recognized after classifying each of multiple keywords into appropriate categories. .

Note that a co-occurring keyword is a keyword that is searched at the same time as or close to the keyword to be searched. For example, a co-occurring keyword is a keyword related to a keyword to be searched, a keyword used for narrowing down search, or the like. A keyword that is frequently searched at the same time as or near the search target keyword is a co-occurring keyword that has a strong co-occurrence relationship with the search target keyword. The number of co-occurring keywords is not limited to one, and may be plural. For example, when three or more keywords are used at the same time or close to each other, the second and subsequent keywords may be co-occurring keywords of the first keyword.

For example, as shown in FIG. 1, the terminal device 10 determines a co-occurrence relationship between keywords by linking nodes indicating the respective keywords based on simultaneously searched (simultaneously input) keywords, the closeness of the searched times, and the like. Generate and display a map of co-occurring keywords that indicate Keywords having a co-occurrence relationship are mapped to close positions. Here, a node indicating a keyword is displayed as a figure (circle, rectangle, etc.), an icon, or the like. Also, the search volume of a keyword is represented by the size of a node. Co-occurrence relationships between keywords are represented by links connecting nodes.

The information providing device 100 is an information processing device that provides an information providing service DS1 that provides search information related to searched keywords to an unspecified number of users. The information providing device 100 cooperates with the terminal device 10 of each user U, and provides the terminal device 10 of each user U with API services and the like for various applications and various data. and is realized by a server device, a cloud system, or the like.

In the present embodiment, the information providing apparatus 100 classifies keywords searched by an unspecified number of users using a search engine or the like into corresponding categories in advance, and calculates the search volume (the number of searches) for each keyword. Measure. At this time, the information providing apparatus 100 may create a learned model in advance by machine learning, which classifies and outputs a keyword into an appropriate category when the keyword is input. In addition, the information providing apparatus 100 presumes that keywords searched at the same time or close to each other are co-occurring keywords. Furthermore, the information providing apparatus 100 may calculate the degree of co-occurrence between keywords based on the number of times (frequency) that the keywords are searched at the same time or at close times, the closeness of the times when they were searched, and the like. Then, when the user U operates the terminal device 10 and uses a website or an application for map mapping of co-occurring keywords, information about categories and search volumes for each keyword and co-occurrence relationships between keywords is provided. do.

Also, the information providing device 100 may be an information processing device that provides some web service online to the terminal device 10 of each user U. FIG. For example, the information providing apparatus 100 provides web services such as Internet connection, search service, SNS (Social Networking Service), electronic commerce, electronic payment, online games, online banking, online trading, accommodation/ticket reservation, video/music distribution, and the like. service may be provided. In practice, the information providing apparatus 100 may cooperate with various servers that provide web services as described above and mediate web services.

Note that the information providing device 100 can acquire various types of information about the user U. FIG. For example, the information providing device 100 acquires information about attributes of the user U, such as the user's U gender, age, and area of residence. The information providing apparatus 100 stores and manages identification information indicating the user U (user ID, etc.) and information related to the attributes of the user U. FIG.

Further, the information providing apparatus 100 acquires various types of history information (log data) indicating actions of the user U from the terminal apparatus 10 of the user U or from various servers based on the user ID or the like. For example, the information providing device 100 acquires a location history, which is a history of the user U's location and date and time, from the terminal device 10 . The information providing apparatus 100 also acquires a search history, which is a history of search queries input by the user U, from a search server (search engine). Further, the information providing apparatus 100 acquires a viewing history, which is a history of content viewed by the user U, from the content server. In addition, the information providing apparatus 100 acquires the purchase history, which is the history of product purchases of the user U, from the electronic commerce server or the settlement server. Further, the information providing apparatus 100 may acquire the exhibition history and sales history, which are the history of the user U's exhibition in the marketplace, from the electronic commerce server or the settlement server. In addition, the information providing apparatus 100 acquires the posting history, which is the posting history of the user U, from a posting server that provides a word-of-mouth posting service or an SNS server.

In the present embodiment, the keywords searched by an unspecified number of users using a search engine or the like may be keywords included in the various types of history information (keywords appearing in the history information).

[1-1. basic action〕
For example, as shown in FIG. 1, the information providing device 100 acquires search information related to the search history in the information providing service DS1 of the user U (step S1). When the user U's search information is acquired, the information providing apparatus 100 estimates, for each search target keyword, a narrowing condition for narrowing down the information on the keyword map related to the search target keyword by the user U based on the acquired search information. (Step S2). For example, in FIG. 1, the information providing apparatus 100, based on the search information of the user U, for the search target keyword "cosmetics company #1," A narrowing-down condition that the person's age is "twenties" is estimated.

Subsequently, after estimating the narrowing conditions for each keyword to be searched, the information providing apparatus 100 generates information about a keyword map related to the keywords to be searched narrowed down by the estimated narrowing conditions, and pre-stores information about the generated keyword map. Store (step S3). For example, in FIG. 1, the information providing apparatus 100 sets the keyword map KM1 related to the search target keyword "cosmetics company #1" as the narrowing condition that the sex of the searcher is "female" and that the age of the searcher is "20. Information about the keyword map KM2 narrowed down by the narrowing-down condition of """ is generated, and the information about the generated keyword map KM2 is stored in advance.

The user U uses the terminal device 10 to access a website for map mapping of co-occurring keywords provided by the information providing service DS1 or to start an application, and input on the screen for map mapping of co-occurring keywords. A keyword to be searched is entered in the column (input box) (step S4). In addition, the keywords to be entered are keywords that have been searched by an unspecified number of users in search engines, etc. (or there is a possibility that they are), and any character string such as words, combinations thereof, or phrases is. In FIG. 1, the user U uses the terminal device 10 to input "cosmetics company #1", which is a keyword indicating the company name of a cosmetics company, as a keyword to be searched.

Subsequently, the information providing device 100 provides the terminal device 10 with information necessary for controlling screen display for map mapping of co-occurring keywords (step S5). Alternatively, the information providing apparatus 100 cooperates with the terminal apparatus 10 to provide various data and instructions (commands, etc.) for controlling screen display for map mapping of co-occurring keywords, Control screen display.

Subsequently, the terminal device 10 cooperates with the information providing device 100 to display a keyword map indicating a co-occurrence network of categories related to the search target keyword input by the user U on the map mapping screen of the co-occurring keyword. display (step S6). In FIG. 1, the terminal device 10 displays a keyword map KM1 related to the search target keyword "cosmetics company #1" input by the user U. FIG.

Subsequently, using the terminal device 10, the user U uses the terminal device 10 to select from among the narrowing conditions displayed on the map mapping screen of the co-occurring keyword, a narrowing condition that the sex of the searcher is "female", A narrowing-down condition that the age of the searcher is "twenties" is selected (step S7).

Subsequently, the information providing device 100 provides the terminal device 10 with information necessary for controlling the screen display for map mapping of the co-occurring keywords narrowed down by the narrowing-down condition selected by the user U. (Step S8). In FIG. 1, the information providing apparatus 100 supplies the terminal device 10 with a pre-generated and stored narrowing condition that the searcher's sex is "female" and a searcher's age of "twenties". provides information on a keyword map related to the keyword "cosmetics company #1" that is the search target narrowed down by the narrowing-down condition of ".

Subsequently, the terminal device 10 cooperates with the information providing device 100 to display a refinement condition that the searcher's gender is "female" and a searcher's age of "20" on the map mapping screen for co-occurring keywords. A keyword map KM2 relating to the keyword "cosmetics company #1" to be searched narrowed down by the narrowing-down condition "" is displayed (step S9).

As described above, the information providing apparatus 100, based on the search information about the search history of the user U who uses the information providing service DS1 that provides search information about keywords searched by an unspecified number of searchers, U estimates the narrowing conditions (“female” and “twenties” in FIG. 1) for narrowing down search results related to the search target keyword (“cosmetics company #1” in FIG. 1) for each search target keyword. Further, the information providing apparatus 100 stores in advance the search results narrowed down by the estimated narrowing-down conditions (in FIG. 1, information on the keyword map KM2). Then, when the information providing apparatus 100 receives from the user U a request to display the search results narrowed down by the estimated narrowing-down conditions, the information providing apparatus 100 sends the pre-stored search results (information about the keyword map KM2 in FIG. 1) to the terminal device. 10 to display.

In this way, the information providing apparatus 100 predicts, for each keyword to be searched, narrowing conditions for search results that the user U will select from now on, based on the past search history of the user U in the information providing service DS1. Further, the information providing apparatus 100 generates search results narrowed down by a previously predicted narrowing-down condition for each keyword to be searched, and stores the generated narrowed down search results in advance. Then, when the information providing apparatus 100 receives from the user U a request to display the search results narrowed down by the narrowing-down condition, the information providing apparatus 100 displays the narrowed-down search results stored in advance. As a result, since the information providing apparatus 100 generates and stores the narrowed down search results in advance, the narrowed down search results are generated and displayed after receiving the narrowing conditions. Search results can be displayed at high speed. Therefore, the information providing device 100 can provide useful information at a higher speed.

[2. Configuration example of information processing system]
Next, the configuration of the information processing system 1 including the information providing device 100 according to the embodiment will be described with reference to FIG. 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 device 100. As shown in FIG. These various devices are communicatively connected via a network N by wire or wirelessly. The network N is, for example, a LAN (Local Area Network) or a WAN (Wide Area Network) such as the Internet.

Also, the number of devices included in the information processing system 1 shown in FIG. 2 is not limited to the illustrated one. For example, only one terminal device 10 is shown in FIG. 2 for simplification of illustration, but this is only an example and is not limited, and two or more devices may be provided.

The terminal device 10 is an information processing device used by the user U. FIG. For example, the terminal device 10 may be 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 with a communication function, a car navigation system, a smart watch, or a head-mounted display. and other wearable devices, smart glasses, and the like.

In addition, the terminal device 10 is compatible with wireless communication networks such as LTE (Long Term Evolution), 4G (4th Generation), 5G (5th Generation: fifth generation mobile communication system), Bluetooth (registered trademark), wireless LAN (Local It is possible to communicate with the information providing apparatus 100 by connecting to the network N via short-range wireless communication such as Area Network).

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

[3. Configuration example of terminal device]
Next, the configuration of the terminal device 10 will be described using FIG. FIG. 3 is a diagram showing a configuration example of the terminal device 10. As shown in FIG. 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. Prepare.

(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 apparatus 100 via the network N. FIG. For example, the communication unit 11 is implemented by a NIC (Network Interface Card), an antenna, or the like.

(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 display, but is not limited to this.

In addition, the display unit 12 displays the keyword map received from the information providing apparatus 100 and the narrowed-down keyword map under the control of the display control unit 34 .

(Input unit 13)
The input unit 13 is an input device that receives various operations from the user U. Further, the input unit 13 has, for example, buttons and the like for inputting characters, numbers, and the like. Moreover, when the display unit 12 is a touch panel display, a part of the display unit 12 functions as the input unit 13 . Note that the input unit 13 may be a microphone or the like that receives voice input from the user U. FIG. The microphone may be wireless.

(Positioning unit 14)
The positioning unit 14 receives signals (radio waves) transmitted from GPS (Global Positioning System) satellites, and based on the received signals, position information (for example, latitude and longitude). That is, the positioning unit 14 positions the position of the terminal device 10 . GPS is merely an example of GNSS (Global Navigation Satellite System).

Also, the positioning unit 14 can measure the position by 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 described below as auxiliary positioning means for position correction and the like.

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

(beacon positioning)
The positioning unit 14 may also use the Bluetooth (registered trademark) function of the terminal device 10 to measure the position. For example, the positioning unit 14 measures the position of the terminal device 10 by connecting with a beacon transmitter connected by the Bluetooth (registered trademark) function.

(geomagnetic positioning)
Further, the positioning unit 14 positions the position of the terminal device 10 based on the geomagnetism pattern of the structure measured in advance and the geomagnetic sensor provided in the terminal device 10 .

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

The positioning unit 14 may measure the position of the terminal device 10 using one or a combination of the positioning means described above, if necessary.

(Sensor unit 20)
The sensor unit 20 includes various sensors mounted on or connected to the terminal device 10 . The connection may be wired connection or wireless connection. 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 atmospheric pressure sensor 23, an air temperature sensor 24, a sound sensor 25, an optical sensor 26, a magnetic sensor 27, and an image sensor ( camera) 28.

The sensors 21 to 28 described above are only examples and are not limited. That is, the sensor unit 20 may be configured to include a part 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 physical movements of the terminal device 10 such as movement direction, speed, and acceleration of the terminal device 10 . The gyro sensor 22 detects physical movements of the terminal device 10 such as inclination in three axial directions based on the angular velocity of the terminal device 10 and the like. The atmospheric pressure sensor 23 detects the atmospheric pressure around the terminal device 10, for example.

Since the terminal device 10 includes the above-described acceleration sensor 21, gyro sensor 22, barometric pressure sensor 23, etc., techniques such as pedestrian dead-reckoning (PDR: Pedestrian Dead-Reckoning) using these sensors 21 to 23, etc. , the position of the terminal device 10 can be determined. This makes it possible to acquire indoor position information that is difficult to acquire with a positioning system such as GPS.

For example, a pedometer using the acceleration sensor 21 can calculate the number of steps, walking speed, and distance walked. Further, by using the gyro sensor 22, it is possible to know the traveling direction, the direction of the line of sight, and the inclination of the body of the user U. Also, from the atmospheric pressure detected by the atmospheric pressure sensor 23, the altitude at which the terminal device 10 of the user U is present and the number of floors can be known.

The temperature sensor 24 detects the temperature around the terminal device 10, for example. The sound sensor 25 detects sounds around the terminal device 10, for example. The optical sensor 26 detects the illuminance around the terminal device 10 . The magnetic sensor 27 detects, for example, geomagnetism around the terminal device 10 . The image sensor 28 captures an image around the terminal device 10 .

The atmospheric pressure sensor 23, the temperature sensor 24, the sound sensor 25, the optical sensor 26, and the image sensor 28 described above detect the atmospheric pressure, temperature, sound, and illuminance, respectively, or capture an image of the surroundings to detect the terminal device 10. It is possible to detect the surrounding environment and situations. In addition, it is possible to improve the accuracy of the location information of the terminal device 10 based on the surrounding environment and situation of the terminal device 10 .

(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, an input/output port, and various circuits. Also, 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 , a processing unit 33 and a display control unit 34 .

(Sending unit 31)
The transmission unit 31 receives, for example, 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, and information measured by the positioning unit 14. The position information of the terminal device 10 and the like can be transmitted to the information providing device 100 via the communication unit 11 .

(Receiver 32)
The receiving unit 32 can receive various information provided by the information providing apparatus 100 and requests for various information from the information providing apparatus 100 via the communication unit 11 . For example, the receiving unit 32 receives information on the narrowed down keyword map from the information providing apparatus 100 via the communication unit 11 .

(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 various types of information transmitted by the transmitting unit 31 and various types of information received by the receiving unit 32 from the information providing apparatus 100 to the display unit 12 for display. For example, the processing unit 33 outputs information about the narrowed down keyword map received by the receiving unit 32 to the display unit 12 for display.

(Display control unit 34)
The display control unit 34 generates a map of co-occurring keywords showing the co-occurrence relationship between keywords by linking the nodes showing each keyword based on the keywords searched (simultaneously input) and the closeness of the searched time. to display. Keywords having a co-occurrence relationship are mapped to close positions. Here, a node indicating a keyword is displayed as a figure (circle, rectangle, etc.), an icon, or the like. Also, the search volume of a keyword is represented by the size of a node. Co-occurrence relationships between keywords are represented by links connecting nodes. For example, the display control unit 34 generates and displays a narrowed down keyword map. Note that the display control unit 34 may be part of the processing unit 33 .

Further, the display control unit 34 reproduces the keyword map showing the co-occurrence network of the categories in time series when the user U performs an operation to reproduce the keyword map showing the co-occurrence network of the categories in time series. For example, when the user U presses a "time-series playback" button displayed on the screen, the display control unit 34 time-series-plays back a keyword map showing co-occurrence networks of categories in a time slider format. In the time-series-reproduced keyword map, the size of the node indicating the keyword changes according to the temporal change in the search volume of the keyword. In addition, the distance between nodes (link length) also changes according to the temporal change of the co-occurrence relationship between keywords.

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

[4. Configuration example of information providing device]
Next, the configuration of the information providing device 100 according to the embodiment will be described using FIG. 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 has a communication section 110, a storage section 120, and a control section .

(Communication unit 110)
The communication unit 110 is realized by, for example, a NIC (Network Interface Card) or the like. Also, the communication unit 110 is connected to the network N (see FIG. 3) by wire or wirelessly.

(storage unit 120)
The storage unit 120 is realized by, for example, a semiconductor memory device such as a RAM (Random Access Memory) or flash memory, or a storage device such as a hard disk or an optical disk. As shown in FIG. 4 , storage unit 120 has user information database 121 , history information database 122 , and keyword information database 123 .

(User information database 121)
The user information database 121 stores various information about the user U. FIG. For example, the user information database 121 stores various information such as user U attributes. FIG. 5 is a diagram showing an example of the user information database 121. As shown in FIG. In the example shown in FIG. 5, the user information database 121 has items such as "user ID (Identifier)", "age", "gender", "home", "place of work", and "interest".

“User ID” indicates identification information for identifying the user U. "Age" indicates the age of the user U identified by the user ID. Note that the “age” may be a specific age of the user U identified by the user ID, such as 35 years old. "Gender" indicates the gender of the user U identified by the user ID.

"Home" indicates location information of the home of the user U identified by the user ID. In the example shown in FIG. 5, "home" is represented by an abstract code such as "LC11", but may be latitude/longitude information or the like. Also, for example, "home" may be an area name or an address.

"Place of work" indicates location information of the place of work (school in the case of a student) of the user U identified by the user ID. In the example shown in FIG. 5, the "place of work" is illustrated as an abstract code such as "LC12", but may be latitude/longitude information or the like. Also, for example, the "place of work" may be an area name or an address.

"Interest" indicates the interest of the user U identified by the user ID. That is, "interest" indicates an object in which the user U identified by the user ID is highly interested. For example, the "interest" may be a search query (keyword) that the user U has entered into a search engine and searched for. In the example shown in FIG. 5, one "interest" is shown for each user U, but a plurality of "interests" may be shown.

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

Here, in the example shown in FIG. 5 , abstract values such as “U1”, “LC11” and “LC12” are used, but “U1”, “LC11” and “LC12” have specific values. It is assumed that information such as character strings and numerical values is stored. Hereinafter, abstract values may also be illustrated in diagrams relating to other information.

The user information database 121 is not limited to the above, and may store various types of information depending on the purpose. For example, the user information database 121 may store various types of information regarding the user U's terminal device 10 . In addition, the user information database 121 stores user U's demographics (demographic attributes), psychographics (psychological attributes), geographics (geographical attributes), behavioral attributes (behavioral attributes), etc. Information about attributes may be stored. For example, the user information database 121 includes name, family structure, hometown (local), occupation, position, income, qualification, residence type (detached house, condominium, etc.), presence or absence of car, commuting time, commuting time, commuting time. Information such as routes, commuter pass sections (stations, lines, etc.), frequently used stations (other than the nearest station to your home or place of work), lessons (places, time zones, etc.), hobbies, interests, lifestyle, etc. may

(History information database 122)
The history information database 122 stores various types of information related to history information (log data) indicating user U's actions. FIG. 6 is a diagram showing an example of the history information database 122. As shown in FIG. 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” indicates identification information for identifying the user U. "Position history" indicates a position history, which is a history of the user's U position and movement. Also, "search history" indicates a search history that is a history of search queries input by the user U. FIG. "Browsing history" indicates a browsing history that is a history of contents browsed by the user U. FIG. "Purchase history" indicates the purchase history of the user U's purchases. In addition, “posting history” indicates a posting history that is a history of posts by the user U. FIG. In addition, the “posting history” may include questions about user U's property.

For example, in the example shown in FIG. 6, the user U identified by the user ID "U1" moves along the "location history #1", searches along the "search history #1", It indicates that the content was browsed according to the "history #1", the predetermined product etc. was purchased at the predetermined store according to the "purchase history #1", and the content was posted according to the "posting history #1".

Here, in the example shown in FIG. 6, abstract history such as "U1", "location history #1", "search history #1", "browsing history #1", "purchase history #1", and "posting history #1" "U1", "location history #1", "search history #1", "browsing history #1", "purchase history #1" and "posting history #1" , information such as specific character strings and numerical values are stored.

Note that the history information database 122 may store various types of information, not limited to the above, depending on the purpose. For example, the history information database 122 may store the user U's usage history of a predetermined service. For example, the history information database 122 stores the usage history of the user U in the information providing service DS1.

For example, the history information database 122 stores the search history of the user U in the information providing service DS1. For example, the history information database 122 stores keywords (also referred to as keywords to be searched) that the user U has entered in an input field (input box) on the map mapping screen for co-occurring keywords provided by the information providing service DS1. Remember. The history information database 122 also stores co-occurring keywords that the user U has input into the input field (input box) together with the search target keyword for each search target keyword. The history information database 122 also stores keyword maps selected by the user U from selection fields (check boxes, calendar periods, etc.) displayed on the screen for map mapping of co-occurring keywords provided by the information providing service DS1. The narrowing conditions for information related to are stored for each keyword to be searched.

In addition, the history information database 122 may store the user U's store visit history, facility visit history, and the like. Also, the history information database 122 may store a history of user U's electronic payment and the like.

(Keyword information database 123)
The keyword information database 123 stores various information related to keywords searched by an unspecified number of users. FIG. 7 is a diagram showing an example of the keyword information database 123. As shown in FIG. In the example shown in FIG. 7, the keyword information database 123 has items such as "keyword", "co-occurrence keyword", "category", "search volume", and "degree of co-occurrence".

"Keyword" indicates a keyword searched by an unspecified number of users using a search engine or the like. Also, "co-occurring keyword" indicates a co-occurring keyword of the keyword. For example, a co-occurring keyword is a keyword that is searched at the same time as or close to the keyword in question. Also, when there is no co-occurring keyword (eg, when a search is performed using only a single keyword), the column for that co-occurring keyword is blank. "Category" indicates the category to which the keyword belongs. Note that the categories may be classified by a set of the keyword and the co-occurring keyword. "Search volume" indicates the search volume (the number of searches) of the keyword. Note that the search volume may be measured for each set of the keyword and the co-occurring keyword. In this case, the total value of the search volumes measured for each set of the keyword and the co-occurring keyword is the search volume of the keyword itself. "Co-occurrence degree" indicates the degree of co-occurrence between the keyword and the co-occurrence keyword. Note that the degree of co-occurrence may be calculated based on the search volume of the keyword. In addition, when there is no co-occurrence keyword (when a search is performed using only a single keyword, etc.), the co-occurrence degree column is blank.

For example, in the example shown in FIG. 7, "keyword #1" is searched together with "co-occurring keyword #11", is classified into "category #11", has "search volume #11", and is associated with the co-occurring keyword. The degree of co-occurrence is “co-occurrence #11”.

Here, in the example shown in FIG. 7, abstract values such as "keyword #1", "co-occurrence keyword #11", "category #11", "search volume #11", and "co-occurrence degree #11" are , specific character strings and numerical values and other information are stored.

It should be noted that the keyword information database 123 may store various types of information, not limited to the above, depending on the purpose. For example, the keyword information database 123 may store information on the total value of search volumes (the search volume of the keyword itself) measured for each set of the keyword and the co-occurring keyword. Further, the keyword information database 123 may store information on categories of co-occurring keywords together with the categories of the keywords. The keyword information database 123 may also store information on the number of PV (Page Views), CTR (Click Through Rate), CVR (Conversion Rate), etc. of the website selected as the search result of the keyword.

Further, the storage unit 120 stores in advance the search results narrowed down by the narrowing-down conditions estimated by the estimation unit 132 . Specifically, the storage unit 120 stores in advance search results narrowed down by a narrowing-down condition in which the priority estimated by the estimation unit 132 exceeds a predetermined threshold. More specifically, the storage unit 120 pre-stores, as an example of search results, information on keyword maps narrowed down by the narrowing-down conditions estimated by the estimation unit 132 (hereinafter also referred to as narrowed down keyword maps).

For example, the storage unit 120 stores in advance information about keyword maps narrowed down by the co-occurrence keywords estimated by the estimation unit 132 . Further, the storage unit 120 stores in advance information related to the keyword map narrowed down by the searcher's attribute information estimated by the estimation unit 132 . In addition, the storage unit 120 stores in advance information regarding the keyword map narrowed down by the gender of the searcher estimated by the estimation unit 132 . In addition, the storage unit 120 stores in advance information related to the keyword map narrowed down by the age of the searcher estimated by the estimation unit 132 . In addition, the storage unit 120 stores in advance information related to the keyword map narrowed down by the searcher's residential area estimated by the estimation unit 132 . In addition, the storage unit 120 stores in advance information about the keyword map narrowed down by the search date estimated by the estimation unit 132 .

(control unit 130)
Returning to FIG. 4, the description is continued. The control unit 130 is a controller, and 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 controls the information providing apparatus 100. Various programs (corresponding to an example of an information processing program) stored in the internal storage device are executed by using a storage area such as a RAM as a work area. In the example shown in FIG. 4 , the control unit 130 has an acquisition unit 131 , an estimation unit 132 , a measurement unit 133 , a reception unit 134 , an extraction unit 135 and a provision unit 136 .

(Acquisition unit 131)
Acquisition unit 131 acquires, from various servers and the like via communication unit 110, information related to keywords used by user U for searching with a search engine or the like.

At this time, the acquiring unit 131 acquires various information about the user U from the terminal device 10, various servers, and the like via the communication unit 110. FIG. For example, the acquisition unit 131 acquires information indicating the user U (user ID, etc.), location information of the user U, attribute information of the user U, and the like. Acquisition unit 131 then registers in user information database 121 of storage unit 120 .

In addition, the acquisition unit 131 acquires various types of history information (log data) indicating actions of the user U from various servers and the like via the communication unit 110 . Acquisition unit 131 then registers in history information database 122 of storage unit 120 .

The keyword used by the user U for searching with a search engine or the like may be a keyword included in various types of history information (keyword appearing in history information).

(Estimation unit 132)
The estimation unit 132 estimates a co-occurring keyword for each keyword searched by an unspecified number of users using a search engine or the like. For example, the estimation unit 132 estimates keywords searched at the same time or at close times as co-occurring keywords. Then, the estimating unit 132 registers in the keyword information database 123 of the storage unit 120 .

In addition, the estimation unit 132 estimates a corresponding category for each keyword searched by an unspecified number of users using a search engine or the like. Then, the estimating unit 132 registers in the keyword information database 123 of the storage unit 120 . For example, the estimating unit 132 compares and collates the acquired keyword with keywords classified into categories in advance, and classifies the acquired keyword into the corresponding category of the keyword. At this time, the estimating unit 132 may create a learned model in advance by machine learning, which, when a keyword is input, classifies it into an appropriate category and outputs it.

For example, the estimation unit 132 creates a trained model for classifying keyword categories by machine learning using RNN (Recurrent Neural Network), LSTM (Long short-term memory), or the like. As for the model, a model of any type and any format can be adopted. For example, the estimation unit 132 may adopt SVM (Support Vector Machine) or DNN (Deep Neural Network) as a model. Here, DNN may be CNN (Convolutional Neural Network) or RNN. The model may also be a model realized by combining a plurality of models, such as a model combining CNN and RNN.

Note that the RNN and LSTM may be neural networks based on an attention mechanism. Also, the estimation unit 132 may use a similar natural language processing model. Data mining and other machine learning algorithms may also be used. The estimating unit 132 can perform model learning using the various learning methods described above.

In practice, the estimating unit 132 may estimate the keyword category based on a combination of the keyword and the co-occurring keyword instead of the keyword alone.

Also, the estimation unit 132 estimates, for each keyword to be searched, a narrowing condition for narrowing down the search results related to the keyword to be searched by the user U, based on the search information about the search history of the user U in the information providing service DS1. For example, the estimation unit 132 estimates, for each keyword to be searched, a narrowing condition for narrowing down the information related to the keyword map related to the keyword to be searched by the user U, as an example of the search results.

Specifically, the estimation unit 132 refers to the history information database 122 to acquire search information related to the search history of the user U in the information providing service DS1. For example, the estimating unit 132, as an example of the search information, the search target keyword input by the user U in the input field (input box) on the map mapping screen for co-occurring keywords provided by the information providing service DS1. get. The estimating unit 132 also acquires, for each search target keyword, a co-occurring keyword input by the user U into the input field (input box) together with the search target keyword, as an example of search information. The estimating unit 132 also selects selection fields (check boxes, calendar periods, etc.) displayed on the screen for map mapping of co-occurring keywords provided by the information providing service DS1 for the user U as an example of search information. Get the narrowing conditions for information related to the keyword map selected from for each keyword to be searched.

Next, based on the acquired search information, the estimation unit 132 estimates, for each search target keyword, a narrowing condition for narrowing down the information on the keyword map related to the search target keyword by the user U. At this time, the estimating unit 132 uses machine learning in advance to output, for each keyword to be searched, narrowing conditions for narrowing down the keyword map related to the keyword for which the user U inputs search information about the search history of the user U. You may create a trained model that Next, the estimating unit 132 estimates, for each keyword to be searched, a narrowing condition for narrowing down the information related to the keyword map related to the keyword to be searched by the user U from the acquired search information using the learned model.

Further, for example, based on the acquired search information, the estimation unit 132 searches for co-occurring keywords that the number of times the user U has entered the search target keyword into the input field (input box) exceeds a predetermined number of times within a predetermined period. Identify by target keyword. After specifying the co-occurring keyword, the estimating unit 132 then estimates that the specified co-occurring keyword is a narrowing-down condition used by the user U to narrow down the information on the keyword map related to the keyword to be searched. Subsequently, the estimating unit 132 generates information about a keyword map related to the search target keywords narrowed down by the co-occurring keywords estimated to be the narrowing-down condition. Subsequently, the estimating unit 132 stores information about the generated keyword map in the storage unit 120 in advance.

In addition, based on the acquired search information, the estimation unit 132 determines the attribute information of the searcher whose number of times the user U has selected for narrowing down the information on the keyword map related to the keyword to be searched exceeds a predetermined number of times within a predetermined period. Identify by keyword to search for. For example, the estimating unit 132, as an example of attribute information of the searcher, identifies the sex of the searcher who has selected more than a predetermined number of times within a predetermined period of time to narrow down the information on the keyword map related to the keyword to be searched by the user U. Identify by keyword to search for. In addition, for example, the estimation unit 132, as an example of the attribute information of the searcher, selects the number of times the user U has selected for narrowing down the information on the keyword map related to the keyword to be searched more than a predetermined number of times within a predetermined period. Specify the age for each keyword to be searched. In addition, for example, the estimation unit 132, as an example of the attribute information of the searcher, selects the number of times the user U has selected for narrowing down the information on the keyword map related to the keyword to be searched more than a predetermined number of times within a predetermined period. Identify the area of residence for each keyword to be searched.

Subsequently, when specifying the attribute information of the searcher, the estimation unit 132 estimates that the specified attribute information of the searcher is a narrowing-down condition used by the user U to narrow down the information on the keyword map related to the keyword to be searched. do. Subsequently, the estimating unit 132 generates information about a keyword map related to keywords to be searched narrowed down by the attribute information of the searcher estimated to be the narrowing-down condition. Subsequently, the estimating unit 132 stores information about the generated keyword map in the storage unit 120 in advance.

In addition, based on the acquired search information, the estimation unit 132 selects a search date for which the user U has selected more than a predetermined number of times within a predetermined period to narrow down the information on the keyword map related to the search target keyword. Identify by keyword. For example, the estimating unit 132 selects, as an example of a search date, a period of search dates that the user U has specified more than a predetermined number of times within a predetermined period (for example, one day before the specified date, one week before the specified date). every week, every week from the specified date to one month ago, every month from the specified date to one year ago, etc.) for each keyword to be searched. After specifying the period, the estimation unit 132 then estimates that the search date corresponding to the specified period is a narrowing-down condition used by the user U to narrow down the information on the keyword map related to the keyword to be searched. Subsequently, the estimating unit 132 generates information about a keyword map related to keywords to be searched narrowed down by the search date estimated to be the narrowing-down condition. Subsequently, the estimating unit 132 stores information about the generated keyword map in the storage unit 120 in advance.

Note that the estimation unit 132 may estimate a narrowing-down condition that combines a plurality of estimated narrowing-down conditions. For example, the estimating unit 132 determines that the searcher's attribute information includes gender of "female" and age of "twenties" as a narrowing-down condition used by the user U to narrow down the information about the keyword map related to the keyword to be searched. A narrowing-down condition such as Further, for example, the estimating unit 132 sets, as a narrowing-down condition used by the user U to narrow down the information on the keyword map related to the keyword to be searched, the gender of the attribute information of the searcher being "female" and the age being "twenties". and the search date is every day from the designated date to one week before.

In addition, the estimation unit 132 estimates the priority of the estimated narrowing condition for each keyword to be searched. Subsequently, the estimating unit 132 generates information about the keyword map narrowed down by the narrowing-down condition whose priority exceeds a predetermined threshold. For example, the estimating unit 132 selects from among a plurality of estimated narrowing conditions the priority of which exceeds a predetermined threshold in order to narrow down the information on the keyword map related to the keyword to be searched by the user U. A narrowing-down condition is selected that exceeds a predetermined number of times within a predetermined period. Subsequently, the estimating unit 132 estimates that a narrowing-down condition whose frequency of selection by the user U exceeds a predetermined threshold is a high-priority narrowing-down condition. Subsequently, the estimating unit 132 generates information about a keyword map related to the keywords to be searched narrowed down by the narrowing-down condition estimated to be the narrowing-down condition with a high priority. Subsequently, the estimating unit 132 stores information about the generated keyword map in the storage unit 120 in advance.

Note that the estimation unit 132 may estimate, for each keyword to be searched, a narrowing-down condition for narrowing down information other than information related to the keyword map. Specifically, based on the search target keyword received from the user U, the providing unit 136 can provide all information related to the search target keyword stored in the storage unit 120 as search results. For example, based on the search target keyword received from the user U, the providing unit 136 selects the search target keyword from among the information stored in the user information database 121, the history information database 122, or the keyword information database 123. can be provided as search results.

That is, the estimating unit 132 may estimate, for each keyword to be searched, a narrowing condition for narrowing down the search results related to the keyword to be searched other than the keyword map among the information stored in the storage unit 120 . For example, the estimating unit 132 sets, for each keyword to be searched, a narrowing condition for narrowing down the search results related to the keyword to be searched among the information stored in the user information database 121, the history information database 122, or the keyword information database 123. can be estimated. For example, the estimating unit 132 uses various information such as co-occurrence keywords, search volumes, categories, and degree of co-occurrence related to the search target keyword stored in the keyword information database 123 as an example of the search results related to the search target keyword, and combinations thereof. Estimates the narrowing conditions for narrowing down the search results for each keyword to be searched. Subsequently, the estimation unit 132 generates search results narrowed down by the estimated narrowing-down conditions. Subsequently, the estimation unit 132 stores the generated narrowed down search results in the storage unit 120 in advance.

(Measuring unit 133)
The measurement unit 133 measures the search volume for each keyword searched by an unspecified number of users using a search engine or the like. For example, the measurement unit 133 counts the number of searches for the keyword based on the information about the keyword used by the user U for searching with a search engine or the like obtained from a search server or the like, the search history, or the like. Then, the measurement unit 133 registers in the keyword information database 123 of the storage unit 120 .

At this time, the measurement unit 133 may measure the search volume of a combination of the keyword and the co-occurring keyword instead of the search volume of the keyword alone.

The measurement unit 133 also calculates the degree of co-occurrence between keywords based on the search volume of a set of a keyword and a co-occurring keyword searched by an unspecified number of users using a search engine or the like. Then, the measurement unit 133 registers in the keyword information database 123 of the storage unit 120 .

(Reception unit 134)
The reception unit 134 receives the keyword input by the user U from the terminal device 10 via the communication unit 110 . For example, the reception unit 134 receives a keyword (also referred to as a keyword to be searched) input in an input field (input box) on a map mapping screen for co-occurring keywords provided by the information providing service DS1.

In addition, the reception unit 134 receives a screen for map mapping of co-occurring keywords provided by the information providing service DS<b>1 from the terminal device 10 via the communication unit 110 by the user U using the input unit 13 of the terminal device 10 . Accepts conditions for narrowing down information about the keyword map entered in the upper input field (input box) or selected from the selection field (check box, calendar period, etc.). For example, the reception unit 134 receives the narrowing-down condition estimated by the estimation unit 132 .

(Extraction unit 135)
The extraction unit 135 compares and collates the received keyword with the keywords registered in the keyword information database 123, and determines the co-occurrence keyword and search volume of the received keyword, the degree of co-occurrence between keywords, and the attribute of the received keyword. Extract information about categories, etc. The category to which the received keyword belongs may be estimated by the estimation unit 132 based on the received keyword. The extracted various information becomes information necessary for controlling the screen display of the UI for map mapping of co-occurring keywords.

(Providing unit 136)
The providing unit 136 provides an information providing service DS1 that provides search information related to keywords searched by an unspecified number of searchers. The information providing service DS1 provides a keyword map that visualizes co-occurrence relationships between co-occurrence keywords that have co-occurrence relationships with keywords searched by an unspecified number of searchers. Specifically, the providing unit 136 provides the terminal device 10 with information necessary for controlling screen display of a UI for map mapping of co-occurring keywords via the communication unit 110 . Alternatively, the providing unit 136 cooperates with the terminal device 10 via the communication unit 110 to provide various data and instructions (commands, etc.) for controlling the screen display, and the UI screen for map mapping of co-occurring keywords. Control display.

In addition, when the receiving unit 134 receives the narrowing-down condition estimated by the estimating unit 132 , the providing unit 136 sends the terminal device 10 via the communication unit 110 the narrowed-down conditions pre-stored in the storage unit 120 . provide information about the keyword map.

[5. Processing procedure]
Next, processing procedures by the terminal device 10 and the information providing device 100 according to the embodiment will be described with reference to FIG. FIG. 8 is a flow chart showing a processing procedure according to the embodiment. The processing procedure described below is repeatedly executed by the control unit 30 of the terminal device 10 and the control unit 130 of the information providing device 100 .

As shown in FIG. 8, the acquisition unit 131 of the information providing apparatus 100 acquires information on keywords used by the user U for searching with a search engine or the like from various servers or the like via the communication unit 110 (step S101).

Subsequently, the estimating unit 132 of the information providing apparatus 100 estimates a co-occurring keyword for each keyword searched by an unspecified number of users using a search engine or the like (step S102). For example, the estimation unit 132 estimates keywords searched at the same time or at close times as co-occurring keywords. Then, the estimating unit 132 registers in the keyword information database 123 of the storage unit 120 .

Subsequently, the estimation unit 132 of the information providing apparatus 100 estimates a corresponding category for each keyword searched by an unspecified number of users using a search engine or the like (step S103). For example, the estimating unit 132 uses machine learning to create in advance a trained model that classifies and outputs a keyword into an appropriate category when the keyword is input, inputs the acquired keyword into the trained model, and classifies the keyword into the appropriate category. Categorize and output. At this time, the estimating unit 132 uses machine learning to create in advance a trained model that, when a set of a keyword and a co-occurring keyword is input, classifies it into an appropriate category and outputs it. may be input to the trained model, and may be output after being classified into the corresponding category. Then, the measurement unit 133 registers in the keyword information database 123 of the storage unit 120 .

Subsequently, the measurement unit 133 of the information providing apparatus 100 measures the search volume for each keyword searched by an unspecified number of users using a search engine or the like (step S104). At this time, the measurement unit 133 may measure the search volume of a combination of the keyword and the co-occurring keyword instead of the search volume of the keyword alone. The measurement unit 133 may also calculate the degree of co-occurrence between keywords based on the search volume of a set of a keyword and a co-occurring keyword searched by an unspecified number of users using a search engine or the like. . Then, the measurement unit 133 registers in the keyword information database 123 of the storage unit 120 .

Subsequently, the estimating unit 132 of the information providing device 100 searches for a narrowing condition for narrowing down the information on the keyword map on the keyword to be searched by the user U based on the search information on the search history of the user U in the information providing service DS1. Estimation is performed for each target keyword (step S105). After estimating the narrowing-down conditions, the estimating unit 132 generates information about the keyword map narrowed down by the estimated narrowing-down conditions.

Subsequently, the storage unit 120 of the information providing apparatus 100 stores in advance information regarding the keyword map narrowed down by the narrowing-down condition estimated by the estimation unit 132 (step S106).

Subsequently, when the receiving unit 134 of the information providing device 100 accesses a site that provides the information providing service DS1 or starts an application from the terminal device 10 via the communication unit 110, the user U The input unit 13 receives a keyword to be searched that has been input into an input field (input box) on the screen for map mapping of co-occurring keywords provided by the information providing service DS1 (step S107).

Subsequently, the extracting unit 135 of the information providing apparatus 100 compares and collates the received keyword to be searched with the keywords registered in the keyword information database 123, and extracts co-occurring keywords and search terms of the received keyword to be searched. Information related to the volume, the category corresponding to the received search target keyword, etc. is extracted (step S108). Note that the category corresponding to the received search target keyword may be estimated by the estimation unit 132 described above. Further, if there is information about the degree of co-occurrence between keywords, the extracting unit 135 may also extract information about the degree of co-occurrence.

Subsequently, the processing unit 33 of the terminal device 10 displays a keyword map showing co-occurrence networks of categories on the display unit 12 based on the information received from the information providing device 100 via the receiving unit 32 (step S109). . At this time, the providing unit 136 of the information providing device 100 provides the terminal device 10 with information necessary for controlling the screen display of the UI for map mapping of co-occurring keywords via the communication unit 110. . Alternatively, the providing unit 136 cooperates with the terminal device 10 via the communication unit 110 to provide various data and instructions (commands, etc.) for controlling the screen display, and the UI screen for map mapping of co-occurring keywords. Control display.

Subsequently, the receiving unit 134 of the information providing device 100 receives the co-occurring keyword provided by the information providing service DS1 from the terminal device 10 via the communication unit 110 by the user U using the input unit 13 of the terminal device 10. A condition for narrowing down information about the keyword map entered in the input field (input box) on the map mapping screen or selected from the selection field (check box, calendar period, etc.), which is estimated by the estimation unit 132 The narrowing down condition is accepted (step S110).

Subsequently, the processing unit 33 of the terminal device 10 displays the narrowed down keyword map stored in advance in the storage unit 120 on the display unit 12 based on the information received from the information providing apparatus 100 via the receiving unit 32. (Step S111).

[6. Modification]
The terminal device 10 and the information providing device 100 described above may be implemented in various different forms other than the above embodiments. So, below, the modification of embodiment is demonstrated.

In the above embodiment, the processing executed by the information providing device 100 may actually be executed by the terminal device 10 . That is, the processing may be completed standalone (by the terminal device 10 alone). In this case, it is assumed that the terminal device 10 has the functions of the information providing device 100 in the above embodiment.

In the above embodiment, the estimating unit 132 of the information providing apparatus 100 creates in advance a trained model for estimating and outputting co-occurring keywords when a keyword is input by machine learning. Accepted keywords may be input to a trained model, and co-occurring keywords may be estimated and output.

Further, in the above-described embodiment, the terminal device 10 measures the number of times of keyword input by the user U on the screen for map mapping of co-occurring keywords provided by the information providing service DS1 as the search volume. may In addition, the terminal device 10 may estimate, as co-occurring keywords, keywords input by the user U at the same time or at a close time on the map mapping screen for co-occurring keywords provided by the information providing service DS1. good.

Further, in the above-described embodiment, the estimation unit 132, based on the search information about the search history of the user U in the information providing service DS1, sets the narrowing condition for narrowing down the search results related to the keyword of the search target of the user U to the search target. Although the case of estimating for each keyword has been described, the present invention is not limited to this. Specifically, the estimation unit 132 extracts a search behavior pattern for a predetermined period (for example, up to about 10 minutes after the start) after each user U started using the information providing service DS1. Then, the estimation unit 132 models the extracted search behavior pattern. Specifically, the estimation unit 132 models operation information related to the operation record of the user U on the UI screen of the information providing service DS1 according to the user U attribute information.

For example, the estimating unit 132 extracts, as an example of the user U's attribute information, the industry to which the user U belongs or the search behavior pattern of the user for each industry to which the user U belongs. Subsequently, the estimating unit 132 generates an industry model that models the user's operation information extracted for each industry to which the user U belongs or for each industry to which the user U belongs.

Also, for example, the estimation unit 132 extracts patterns of search behavior of individual users U as an example of user U attribute information. Subsequently, the estimating unit 132 generates a personal model in which the user operation information extracted for each user U is modeled.

Further, for example, the estimation unit 132 searches for other users belonging to the company to which the user U belongs (for example, colleagues of the company to which the user U belongs) as an example of the attribute information of the user U. Extract patterns of behavior. Subsequently, the estimating unit 132 generates a colleague model that models the user's operation information extracted for each of the other users belonging to the company to which the user U belongs.

Also, for example, the estimation unit 132 determines weights for combining the industry model, the individual model, and the colleague model based on the user U's attribute information. For example, if the weights for combining the industry model, the individual model, and the colleague model are w1, w2, and w3, the estimating unit 132 calculates “w1* (industry model)+w2* (individual model)+w3* (colleague model). ], the search behavior pattern of the user U is estimated. Subsequently, the estimation unit 132 estimates the user U's search behavior pattern based on the weights w1, w2, and w3 determined based on the user U's attribute information.

For example, when estimating the search behavior pattern of a new user of a company that has made a new contract with the information providing service DS1, the estimating unit 132 assumes that the search history of the user is small. Based on the information of the industry to which the new user belongs, the search behavior pattern of the new user is estimated. For example, the estimating unit 132 sets the weight w2 of the personal model based on the search information of the new user to "0" and increases the weight w1 of the industry model based on the industry to which the new user belongs. Estimate behavioral patterns. Subsequently, the estimation unit 132 generates narrowed down search results based on the estimated search behavior pattern. Subsequently, the estimation unit 132 stores the generated narrowed down search results in the storage unit 120 in advance. As a result, the information providing apparatus 100 speeds up drawing for the next screen transition.

Further, for example, when the estimating unit 132 estimates the search behavior pattern of an existing user of a company that has an existing contract with the information providing service DS1, the user is accustomed to using the information providing service DS1 and the user Since it is presumed that there are search behavior patterns that depend on individuals in addition to the information on the industry to which they belong, based on the keywords received from existing users and information on the search history of existing users, the search behavior of existing users Infer patterns. For example, the estimating unit 132 increases the weight w2 of the personal model based on the search information of the existing user more than the weight w1 of the industry model based on the industry to which the existing user belongs, so that the existing user's search behavior pattern to estimate Subsequently, the estimation unit 132 generates narrowed down search results based on the estimated search behavior pattern. Subsequently, the estimation unit 132 stores the generated narrowed down search results in the storage unit 120 in advance. As a result, the information providing apparatus 100 speeds up drawing for the next screen transition.

Further, for example, when estimating the search behavior pattern of a new user of a company that has an existing contract with the information providing service DS1, the estimation unit 132 may select other users belonging to the company to which the new user belongs. (For example, a colleague of the company to which the new user belongs) is already using the information providing service DS1, and it is presumed that the new user is newly assigned to a department that uses the information providing service DS1. Therefore, the estimation unit 132 determines that the new user is performing the same work as other users belonging to the company to which the new user belongs, and that other users belonging to the company to which the new user belongs Since it is estimated to be similar to the search behavior pattern, the search behavior pattern of the new user is estimated based on the keywords received from the new user and information on the search histories of other users. For example, the estimation unit 132 estimates the new user's search behavior pattern by increasing the weight w3 of the colleague model more than the weight w1 of the industry model and the weight w2 of the individual model. Subsequently, the estimation unit 132 generates narrowed down search results based on the estimated search behavior pattern. Subsequently, the estimation unit 132 stores the generated narrowed down search results in the storage unit 120 in advance. As a result, the information providing apparatus 100 speeds up drawing for the next screen transition.

In addition, it is presumed that similar analysis methods (that is, search methods) are likely to be used in the same industry, even if the companies are different, for competitive analysis in the same industry. For example, it is assumed that automobile company A searches for search information about other automobile company B in the domestic automobile industry (or vice versa). Therefore, the estimation unit 132 extracts a keyword category (for example, industry) based on the user's search information and the keyword used by the user for the first search (first search). Subsequently, the estimation unit 132 estimates a search behavior pattern (eg, industry model) that matches the extracted category (eg, industry) as the user's search behavior pattern. Subsequently, the estimation unit 132 generates narrowed down search results based on the estimated search behavior pattern. Subsequently, the estimation unit 132 stores the generated narrowed down search results in the storage unit 120 in advance.

[7. effect〕
As described above, the information processing device (the terminal device 10 and the information providing device 100) according to the present application includes the estimation unit 132, the storage unit 120, and the display unit 12. The estimating unit 132 calculates the search results related to the keyword searched by the user based on the search information related to the search history of the user who uses the information providing service that provides search information related to the keyword searched by an unspecified number of searchers. Estimates the narrowing conditions for narrowing down for each keyword to be searched. The storage unit 120 stores in advance the search results narrowed down by the narrowing-down conditions estimated by the estimation unit 132 . The display unit 12 displays the search results stored in advance in the storage unit 120 when receiving from the user a request to display the search results narrowed down by the narrowing-down conditions estimated by the estimation unit 132 .

In this way, the information processing device predicts in advance, for each keyword to be searched, narrowing conditions for search results that the user will select from now on, based on the user's past search history in the information providing service. In addition, the information processing apparatus generates search results narrowed down by a previously predicted narrowing-down condition for each keyword to be searched, and stores the generated narrowed down search results in advance. Then, the information processing device displays the pre-stored narrowed down search results when receiving a display request from the user for the search results narrowed down by the narrowing down condition. As a result, since the information processing device generates and stores the narrowed down search results in advance, the narrowed down search is more difficult than in the case of generating and displaying the narrowed down search results after receiving the narrowing conditions. Results can be displayed at high speed. Therefore, the information processing device can provide useful information at a higher speed.

The estimation unit 132 also estimates the priority of the estimated narrowing condition for each keyword to be searched, based on the search information related to the user's search history. The storage unit 120 stores in advance search results narrowed down by a narrowing-down condition in which the priority estimated by the estimation unit 132 exceeds a predetermined threshold.

As a result, the information processing apparatus stores only the search results narrowed down by the narrowing-down conditions with high priority, for example, so that the load on the system can be reduced. In addition, the information processing device can dynamically change the cache holding amount in accordance with the usage state of the information providing service by the user.

In addition, the estimating unit 132 estimates, as a narrowing condition, a co-occurring keyword that is searched by the user at the same time as the search target keyword for each search target keyword. The storage unit 120 stores in advance the search results narrowed down by the co-occurring keywords estimated by the estimation unit 132 .

As a result, the information processing apparatus receives co-occurrence keywords searched by the user at the same time as the search target keyword, and then generates and displays search results narrowed down by the co-occurrence keywords. The search results narrowed down by can be displayed at high speed.

In addition, the estimation unit 132 estimates, for each search target keyword, attribute information of a searcher selected by the user from the attribute information of searchers who have searched for the target keyword as a narrowing-down condition. The storage unit 120 stores in advance the search results narrowed down by the attribute information of the searcher estimated by the estimation unit 132 .

As a result, the information processing apparatus receives the attribute information of the searcher selected by the user, and then generates and displays the search results narrowed down by the attribute information of the searcher. The search results narrowed down by can be displayed at high speed.

In addition, the estimating unit 132 estimates, for each search target keyword, the sex of the searcher selected by the user among the sexes of the searchers who searched for the search target keyword as a narrowing-down condition. The storage unit 120 stores in advance the search results narrowed down by the searcher's gender estimated by the estimation unit 132 .

As a result, the information processing apparatus receives the gender of the searcher selected by the user, and then generates and displays the search results narrowed down by the gender of the searcher. search results can be displayed at high speed.

In addition, the estimating unit 132 estimates, as a narrowing-down condition, the age of the searcher selected by the user among the ages of the searchers who searched for the search target keyword for each search target keyword. The storage unit 120 stores in advance the search results narrowed down by the age of the searcher estimated by the estimation unit 132 .

As a result, the information processing apparatus receives the age of the searcher selected by the user, and then generates and displays the search results narrowed down by the age of the searcher. search results can be displayed at high speed.

In addition, the estimating unit 132 estimates, as a narrowing-down condition, the residential area of the searcher selected by the user from among the residential areas of the searchers who searched for the search target keyword for each search target keyword. The storage unit 120 stores in advance the search results narrowed down by the searcher's residential area estimated by the estimation unit 132 .

As a result, the information processing apparatus receives the searcher's residence area selected by the user, and then generates and displays the search results narrowed down by the searcher's residence area. The search results narrowed down by can be displayed at high speed.

In addition, the estimating unit 132 estimates, as a narrowing-down condition, the search date selected by the user among the search dates on which the search target keyword was searched for each search target keyword. The storage unit 120 stores in advance the search results narrowed down by the search date estimated by the estimation unit 132 .

As a result, the information processing apparatus generates and displays the search results narrowed down by the search date after accepting the search date selected by the user, and can display the search results narrowed down by the search date at high speed. can be displayed.

In addition, the estimating unit 132 sets, for each search target keyword, a narrowing condition for narrowing down information related to a keyword map that visualizes co-occurrence relationships between keywords having co-occurrence relationships with the search target keyword as search results related to the search target keyword. presume. The storage unit 120 stores in advance information about keyword maps narrowed down by the narrowing-down conditions estimated by the estimation unit 132 . The display unit 12 displays information related to the keyword map stored in advance in the storage unit 120 when receiving a request from the user to display information related to the keyword map narrowed down by the narrowing-down condition estimated by the estimation unit 132 .

As a result, the information processing apparatus displays a narrowed down keyword map stored in advance. Maps can be displayed quickly. Therefore, the information processing device can provide useful information at a higher speed.

[8. Hardware configuration]
Also, the terminal device 10 and the information providing device 100 according to the above-described embodiments are implemented by a computer 1000 configured as shown in FIG. 9, for example. Hereinafter, the terminal device 10 will be described as an example. FIG. 9 is a diagram illustrating an example of a hardware configuration; The computer 1000 is connected to an output device 1010 and an input device 1020, 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 buses. It has a form connected by 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 unit 1030 is implemented by, for example, a CPU (Central Processing Unit), MPU (Micro Processing Unit), ASIC (Application Specific Integrated Circuit), 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 temporarily stores data used for various calculations by the arithmetic device 1030 . The secondary storage device 1050 is a storage device in which data used for various calculations by the arithmetic device 1030 and various databases are registered. State Drive), flash memory, or the like. 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 memory or an SD (Secure Digital) memory card. Also, the secondary storage device 1050 may be a cloud storage (online storage), 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 the output device 1010 that outputs various information such as a display, a projector, and a printer. (Digital Visual Interface), HDMI (registered trademark) (High Definition Multimedia Interface), and other standardized connectors. Also, the input I/F 1070 is an interface for receiving information from various input devices 1020 such as a mouse, keyboard, keypad, buttons, scanner, etc., and is realized by, for example, USB.

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.

Also, 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.

Note that the input device 1020 includes, for example, optical recording media such as CDs (Compact Discs), DVDs (Digital Versatile Discs), PDs (Phase change rewritable discs), magneto-optical recording media such as MOs (Magneto-Optical discs), and tapes. It may be a device that reads information from a medium, a magnetic recording medium, a semiconductor memory, or the like.

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

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

For example, when the computer 1000 functions as the terminal device 10 , the arithmetic device 1030 of the computer 1000 implements the functions of the control unit 30 by executing programs loaded on the primary storage device 1040 . Further, arithmetic device 1030 of computer 1000 may load a program acquired from another device via network I/F 1080 onto primary storage device 1040 and execute the loaded program. Further, the arithmetic unit 1030 of the computer 1000 may cooperate with another device via the network I/F 1080, and call functions, data, etc. of the program from another program of the other device for use.

[9. others〕
Although the embodiments of the present application have been described above, the present invention is not limited by the contents of these embodiments. In addition, the components described above 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 components described above can be combined as appropriate. Furthermore, various omissions, replacements, or modifications of components can be made without departing from the gist of the above-described embodiments.

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

Also, each component of each device illustrated is functionally conceptual, and does not necessarily need to be physically configured as illustrated. In other words, the specific form of distribution and integration of each device is not limited to the illustrated one, and all or part of them can be functionally or physically distributed and integrated in arbitrary units according to various loads and usage conditions. Can be integrated and configured.

For example, the information providing apparatus 100 described above may be implemented by a plurality of server computers, and depending on the function, may be implemented by calling an external platform or the like using an API (Application Programming Interface), network computing, or the like. can be changed flexibly.

Also, the above-described embodiments and modifications can be appropriately combined within a range that does not contradict the processing contents.

Also, the above-mentioned "section, module, unit" can be read as "means" or "circuit". For example, the estimating unit can be read as estimating means or an estimating circuit.

1 information processing system 10 terminal device 12 display unit 34 display control unit 100 information providing device 121 user information database 122 history information database 123 keyword information database 131 acquisition unit 132 estimation unit 133 measurement unit 134 reception unit 135 extraction unit 136 provision unit

Claims (11)

Refinement conditions for narrowing down the search results related to the keyword searched by the user based on the search information related to the search history of the user who uses the information providing service that provides search information related to the keyword searched by an unspecified number of searchers an estimating unit for estimating for each keyword to be searched;
a storage unit that stores in advance the search results narrowed down by the narrowing-down condition estimated by the estimation unit;
a display unit for displaying the search results stored in advance in the storage unit when a display request for the search results narrowed down by the narrowing-down condition estimated by the estimation unit is received from the user;
An information processing device comprising: The estimation unit
estimating the priority of the estimated narrowing condition for each keyword of the search target based on search information related to the search history of the user;
The storage unit
pre-storing the search results narrowed down by the narrowing-down condition whose priority estimated by the estimating unit exceeds a predetermined threshold;
The information processing apparatus according to claim 1, characterized by: The estimation unit
As the narrowing condition, a co-occurring keyword searched by the user at the same time as the search target keyword is estimated for each of the search target keywords,
The storage unit
storing in advance the search results narrowed down by the co-occurring keywords estimated by the estimation unit;
3. The information processing apparatus according to claim 1, wherein: The estimation unit
estimating the attribute information of the searcher selected by the user from among the attribute information of the searcher who searched for the search target keyword as the narrowing condition for each of the search target keywords;
The storage unit
storing in advance the search results narrowed down by the attribute information of the searcher estimated by the estimation unit;
4. The information processing apparatus according to any one of claims 1 to 3, characterized by: The estimation unit
estimating the sex of the searcher selected by the user from among the sexes of searchers who searched for the search target keyword as the narrowing condition for each of the search target keywords;
The storage unit
storing in advance the search results narrowed down by the sex of the searcher estimated by the estimation unit;
5. The information processing apparatus according to claim 4, characterized by: The estimation unit
As the narrowing condition, the age of the searcher selected by the user among the ages of the searchers who searched for the search target keyword is estimated for each of the search target keywords,
The storage unit
storing in advance the search results narrowed down by the age of the searcher estimated by the estimation unit;
6. The information processing apparatus according to claim 4, wherein: The estimation unit
As the narrowing condition, an area of residence of the searcher selected by the user among the residence areas of the searcher who searched for the search target keyword is estimated for each of the search target keywords,
The storage unit
pre-storing the search results narrowed down by the searcher's residential area estimated by the estimation unit;
7. The information processing apparatus according to any one of claims 4 to 6, characterized by: The estimation unit
estimating, as the narrowing condition, the search date selected by the user from search dates on which the search target keyword was searched for each of the search target keywords;
The storage unit
storing in advance the search results narrowed down by the search date estimated by the estimation unit;
8. The information processing apparatus according to any one of claims 1 to 7, characterized by: The estimation unit
estimating, for each of the search target keywords, a narrowing condition for narrowing down information related to a keyword map that visualizes co-occurrence relationships between keywords having a co-occurrence relationship with the search target keyword as search results related to the search target keyword;
The storage unit
pre-storing information about the keyword map narrowed down by the narrowing-down condition estimated by the estimation unit;
The display unit
displaying information about the keyword map stored in advance in the storage unit when a request for display of information about the keyword map narrowed down by the narrowing-down condition estimated by the estimation unit is received from the user;
The information processing apparatus according to any one of claims 1 to 8, characterized by: A computer-executed information processing method comprising:
Refinement conditions for narrowing down the search results related to the keyword searched by the user based on the search information related to the search history of the user who uses the information providing service that provides search information related to the keyword searched by an unspecified number of searchers an estimation step of estimating for each keyword to be searched;
a storage step of pre-storing in a storage unit the search results narrowed down by the narrowing-down conditions estimated in the estimation step;
a display step of displaying the search results stored in advance in the storage unit when a display request for the search results narrowed down by the narrowing conditions estimated in the estimation step is received from the user;
An information processing method comprising: Refinement conditions for narrowing down the search results related to the keyword searched by the user based on the search information related to the search history of the user who uses the information providing service that provides search information related to the keyword searched by an unspecified number of searchers an estimation procedure for estimating for each keyword to be searched;
a storage step of pre-storing in a storage unit the search results narrowed down by the narrowing-down conditions estimated by the estimation step;
a display procedure for displaying the search results stored in advance in the storage unit when a display request for the search results narrowed down by the narrowing conditions estimated by the estimation procedure is received from the user;
An information processing program characterized by causing a computer to execute

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