Information processing device, information processing method, and information processing program

Abstract

To provide an information processor, an information processing method, and an information processing program that create an appropriate list of a volume and a feature.SOLUTION: In an information processing system in which a plurality of terminal devices and a server device are connected via a network, a server device 100 includes an acquisition unit 131, a classification unit 132, an identification unit 133, a reception unit 134, a setting unit 135, a generation unit 136, and a provision unit 137. The identification unit identifies a feature and a volume with respect to an object having a predetermined attribute, which is related to a predetermined query. The reception unit receives selection of a reference object from a graph plotting the object with the volume and the feature. The setting unit sets, on the basis of ratio of the feature and the volume of the selected reference object, a reference point. The generation unit generates ranking in which objects are arranged in descending order of distance between the set reference point and the plotted position of each object. The provision unit provides information on the ranking.SELECTED DRAWING: Figure 6

Description

[Technical Field]

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

[0002] In recent years, with the rapid spread of the Internet, techniques for analyzing various types of information on the Internet have been provided, such as techniques for extracting information on needs for a target provided by a specified business operator based on a search query entered by a user. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2019-32776 Summary of the Invention [Problem to be solved by the invention]

[0004] However, the above-mentioned conventional techniques do not necessarily provide useful information. For example, the above-mentioned conventional techniques only extract information about the needs of the target provided by a specific business, and therefore are unable to create an appropriate list of volume and distinctiveness.

[0005] The present invention has been made in view of the above, and aims to create an appropriate list of volumes and distinctiveness. [Means for solving the problem]

[0006] The information processing device according to the present application includes an identifying unit that identifies a characteristic degree and a volume of a target that is related to a predetermined query and has a predetermined attribute, the characteristic degree and the volume of the target; a receiving unit that receives a selection of a reference target from a graph in which targets are plotted in terms of volume and characteristic degree; a setting unit that sets a reference point based on a ratio of the characteristic degree and the volume of the selected reference target; a generating unit that generates a ranking in which targets are arranged in order of proximity from the set reference point to the position at which each target is plotted; and a providing unit that provides information about the ranking. The identification unit identifies the distinctiveness as a value obtained by dividing the number of times a word indicating the target was input together with the query by the number of times another word was input together with the query, and identifies the number of times a word indicating the target was input together with the query or the number of users who input a word indicating the target together with the query as a volume. It is characterized by: [Effects of the Invention]

[0007] According to one aspect of the embodiment, an appropriate list of volumes and distinctiveness can be created. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is an explanatory diagram showing an overview of an information processing method according to an embodiment. [Figure 2] FIG. 2 is an explanatory diagram showing an overview of user clustering. [Figure 3] FIG. 3 is a diagram showing an example of a graph in which objects are plotted by volume and characteristic degree. [Figure 4] FIG. 4 is a diagram illustrating an example of the configuration of an information processing system according to the embodiment. [Figure 5] FIG. 5 is a diagram illustrating an example of the configuration of a terminal device according to the embodiment. [Figure 6] FIG. 6 is a diagram illustrating an example of the configuration of a server device according to the embodiment. [Figure 7] FIG. 7 is a diagram illustrating an example of the user information database. [Figure 8] FIG. 8 is a diagram illustrating an example of the history information database. [Figure 9] FIG. 9 is a diagram illustrating an example of the reference point information database. [Figure 10] FIG. 10 is a flowchart showing a processing procedure according to the embodiment. [Figure 11] FIG. 11 is a diagram illustrating an example of a hardware configuration. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, an information processing device, an information processing method, and an information processing program according to the present application (hereinafter referred to as "embodiments") will be described in detail with reference to the drawings. Note that the information processing device, the information processing method, and the information processing program according to the present application are not limited to these embodiments. Furthermore, the same components in the following embodiments will be denoted by the same reference numerals, and duplicated descriptions will be omitted.

[0010] [1. Overview of information processing method] First, an overview of an information processing method performed by an information processing device according to an embodiment will be described with reference to Fig. 1. Fig. 1 is an explanatory diagram showing an overview of an information processing method according to an embodiment. Note that Fig. 1 explains an example in which an appropriate list of volumes and feature degrees is created.

[0011] 1, the information processing system 1 includes a terminal device 10 and a server device 100. The terminal device 10 and the server device 100 are connected to each other via a network N (see FIG. 4) in a wired or wireless manner so as to be able to communicate with each other. In this embodiment, the terminal device 10 cooperates with the server device 100.

[0012] The terminal device 10 is a smart device such as a smartphone or tablet terminal used by a user U, and is a portable terminal device capable of communicating with any server device via a wireless communication network such as 4G (Generation (4G)) or LTE (Long Term Evolution) networks. The terminal device 10 has a screen such as a liquid crystal display with a touch panel function, and accepts various operations on displayed data such as content, such as tapping, sliding, and scrolling, performed by the user U with a finger or a stylus. An operation performed on an area of the screen where content is displayed may be considered an operation on the content. The terminal device 10 may be not only a smart device, but also an information processing device such as a desktop PC (Personal Computer) or a notebook PC.

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

[0014] The server device 100 may also be an information processing device that provides some kind of online web service to the terminal device 10 of each user U. For example, the server device 100 may provide the following web services: internet connection, search service, social networking service (SNS), electronic commerce (EC), electronic payment, online games, online banking, online trading, hotel and ticket reservations, video and music distribution, news, maps, route search, route guidance, line information, operation information, and weather forecast. In practice, the server device 100 may cooperate with various servers that provide the above-mentioned web services and act as an intermediary for the web services or may be responsible for processing the web services.

[0015] The server device 100 can acquire user information about the user U. For example, the server device 100 acquires information about the attributes of the user U, such as the gender, age, and residential area of the user U. The server device 100 then stores and manages the information about the attributes of the user U together with identification information (such as a user ID) that identifies the user U.

[0016] The server device 100 also acquires various types of history information (log data) indicating the behavior of the user U from the terminal device 10 of the user U or from various servers based on the user ID, etc. For example, the server 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 server device 100 also acquires a search history, which is a history of search queries entered by the user U, from a search server (search engine). The server device 100 also acquires a browsing history, which is a history of content viewed by the user U, from a content server. The server device 100 also acquires a purchase history (payment history), which is a history of the user U's product purchases and payment processes, from an e-commerce server or a payment processing server. The server device 100 may also acquire a listing history and a sales history, which are a history of the user U's listings on the marketplace, from the e-commerce server or the payment processing server. The server device 100 also acquires a posting history, which is a history of the user U's posts, from a posting server or SNS server that provides a word-of-mouth posting service.

[0017] [1-1. User Clustering] First, the server device 100 clusters users based on the input patterns of search keywords.

[0018] As shown in FIG. 1, the server device 100 extracts target users (step S1). In this embodiment, the server device 100 extracts users who have searched for the same search keyword as target users. For example, the server device 100 extracts users who have searched for keywords including soy meat a certain number of times or more as a user group interested in soy meat (soy meat interest group). The reason for setting the number of times or more as a certain number is to exclude users who are interested in soy meat but have little interest.

[0019] At this time, the server device 100 may accept input of a search query (search keyword) from the terminal device 10 of each user U via the network N (see FIG. 4), collect a log (search history) of the search query of each user U, and extract a user group that has an interest in a particular item. Note that in practice, the server device 100 may obtain information about the search query entered by each user U from the search engine that accepted the input of the search query. In other words, the input of the search query (search keyword) from the terminal device 10 of each user U may be accepted directly or indirectly.

[0020] Next, the server device 100 extracts keywords of interest to the target person (step S2). For example, the server device 100 extracts keywords of interest such as "vegan shampoo," "vegetarian diet," and "muscle training" as keywords of interest to the target group interested in soy meat. Note that the server device 100 excludes "soy meat" from the keywords of interest to the group interested in soy meat. This is because it is already clear that the group interested in soy meat is interested in "soy meat." In other words, the server device 100 extracts search keywords other than "soy meat" as keywords of interest to the group interested in soy meat.

[0021] At this time, the server device 100 may extract (acquire) information about the 5W1H, such as the input of "Who," "When," "Where," "What," "Why," and "How," from the search query log (search history) of each user U. Note that the server device 100 may extract (acquire) information about any or any combination of the 5W1H, rather than all (all elements) of the 5W1H.

[0022] Next, the server device 100 clusters (groups) the target users based on their search trends (step S3). For example, as shown in FIG. 2, the server device 100 classifies the target users who are interested in soy meat into segments such as a "belief" group, a "healthy diet" group, a "diet" group, a "fashion" group, and an "interest in the industry" group. FIG. 2 is an explanatory diagram showing an overview of user clustering. In this way, even a user group who is similarly interested in soy meat (a group interested in soy meat) can be classified into multiple clusters based on their search trends.

[0023] At this time, the server device 100 mechanically classifies the keywords of interest of the extracted users and assigns them to the most frequently searched topic for each user. Note that the server device 100 may also assign them to the most frequently searched topic for each user. Also, one user may belong to multiple clusters.

[0024] [1-2. Create a list of volume and distinctiveness] For example, when creating a list of celebrities who are compatible with each soy meat cluster, the following challenges (1) to (4) arise.

[0025] (1) When sorting by distinctiveness, items with smaller volume will rank higher (= this is often used because the results tend to be more interesting).

[0026] (2) When sorted by volume, the same things usually come out on top in several clusters (= they are boring, so they have a lot of distinctiveness).

[0027] (3) When plotting distinctiveness x volume, there are so many options that people say they don't know which one to choose.

[0028] (4) Furthermore, what is considered appropriate varies depending on the situation and the company / person.

[0029] Therefore, in this embodiment, the server device 100 creates a list that is just right (appropriate) for "volume x distinctiveness."

[0030] 1, the server device 100 receives a predetermined query from the terminal device 10 of the user U via the network N (see FIG. 4) in advance (step S4). The content of the predetermined query may be, for example, a product or a brand.

[0031] Next, the server device 100 identifies the distinctiveness and volume of a target that is related to the specified query and has a specified attribute (step S5). For example, the server device 100 identifies the degree of co-occurrence (distinctiveness) of a plurality of targets with the specified query and the number of users with co-occurrence (volume). At this time, the server device 100 identifies the distinctiveness and volume of the target for each cluster of targets. Note that the target may be a target classified into a plurality of clusters.

[0032] Any method can be used to determine the distinctiveness and volume of a query. For example, the server device 100 determines the distinctiveness by dividing the number of times a word indicating the target is searched together with the query by the number of times another word is searched together with the query. In addition, the server device 100 may determine the distinctiveness by dividing the number of times a word indicating the target is searched together with the query by the number of times another word is searched together with the query. subject The number of times the word indicating subject The volume is the number of users who input the word indicating the above.

[0033] The server device 100 may obtain the distinctiveness by dividing the number of times the user who input the query searched for / viewed / purchased the target by the number of times the user searched for / viewed / purchased other targets, and may obtain the volume by the number of users who searched for / viewed / purchased the target among the users who input the query. Furthermore, searching for / viewing / purchasing the target may be a conversion.

[0034] That is, the server device 100 may use the number of times the user who entered the query performed an action related to the target divided by the number of times the user performed actions related to other targets as the characteristic score, and may use the number of users who performed actions related to the target among the users who entered the query as the volume.

[0035] Next, the server device 100 provides a graph plotting the target by volume and characteristic degree, as shown in (a) of Fig. 3, to the terminal device 10 of the user U via the network N (see Fig. 4) (step S6). Fig. 3 is a diagram showing an example of a graph plotting the target by volume and characteristic degree.

[0036] Next, the server device 100 receives a selection of a reference target from the targets plotted on the graph, as shown in FIG. 3(b), from the terminal device 10 of the user U via the network N (see FIG. 4) (step S7). The number of targets selected may be one or more. When selecting a reference target, the user U may select each target individually, or may specify a range R to select targets within that range R.

[0037] For example, the user U uses the terminal device 10 to select an object (an object desired by the user U) from among the objects plotted on the graph. Alternatively, the user U uses the terminal device 10 to input an object that he / she has as his / her hypothesis (an object that the user U presumes). Then, the server device 100 accepts the selection or input of the reference object by the user U from the terminal device 10 of the user U via the network N (see FIG. 4).

[0038] That is, the user U uses the terminal device 10 to specify "this person" / "this range." Alternatively, the user U uses the terminal device 10 to manually input a "presumed person." At this time, it is also possible to select / specify multiple people. If the manually input person does not exist on the graph, the server device 100 may display similar people through a search or the like.

[0039] Next, the server device 100 calculates the ratio (proportion) between the characteristic score and the volume of the selected reference object, as shown in FIG. 3(b) (step S8). That is, the server device 100 calculates the ratio between the volume and characteristic score of a person who matches the image of the user U. In FIG. 3(b), the ratio between the characteristic score and the volume of the selected reference object is shown as "a:b." Note that, if multiple reference objects are selected, the server device 100 may use any of the average, median, maximum, and minimum values as the ratio between the characteristic score and the volume. Alternatively, the server device 100 may calculate the ratio between the characteristic score and the volume of the selected reference object using a predetermined formula.

[0040] Next, the server device 100 sets a reference point P (starting point) based on the ratio between the volume and characteristic score of the selected reference object (step S9), as shown in (c) of Figure 3. For example, the server device 100 sets the reference point P based on the ratio between the degree of co-occurrence (characteristic score) of the selected reference object and the number of users (volume).

[0041] In this embodiment, the server device 100 selects and sets a reference point P on the reference line such that the ratio between the volume and characteristic score of the selected reference object is the same as the calculated ratio. Note that the server device 100 may select the largest volume of each object's volume (the point at which a horizontal line intersects with the reference line) or the largest characteristic score of each object (the point at which a vertical line intersects with the reference point P).

[0042] Next, as shown in FIG. 3(c), the server device 100 calculates the distance between the set reference point P and the position where each object is plotted (step S10). In FIG. 3(c), the distance between each object and the position where each object is plotted is indicated as d (d1, d2, d3, ...). For example, the server device 100 calculates the distance between the reference point P and information indicating each object in a graph where the objects are plotted, based on the degree of co-occurrence (characteristics) of the selected reference objects and the number of users (volume). Note that the server device 100 changes the reference point P when calculating the distance, or weights the reference point P or the distance, based on the ratio between the volume and characteristics of the selected reference object.

[0043] Next, the server device 100 creates a ranking in which the targets are arranged in order of closest distance (step S11). That is, as shown in FIG. 3(c), the server device 100 sets a reference point P based on the ratio of the volume of a person who matches the image of the user U to the characteristic degree, and creates a ranking based on the distance from that point. For example, the server device 100 creates a ranking based on the shortest distance from the point set as the reference point P. Note that the method of calculating the distance and determining the reference point P can be changed according to the index, such as logarithmic conversion.

[0044] Next, the server device 100 provides, via the network N (see FIG. 4), the terminal device 10 of the user U with ranking information in which the targets are arranged in descending order of distance (step S12). At this time, the server device 100 may provide content in which information indicating each target is arranged in descending order of the calculated distance.

[0045] In this embodiment, the server device 100 may clarify the degree of co-occurrence (characteristics) with respect to a specified query. The server device 100 may also clarify the number of users with co-occurrence (volume). The server device 100 may also provide a graph and accept the selection of a reference target. The server device 100 may also set a reference point P corresponding to the maximum value of the volume. The server device 100 may also set a reference point P corresponding to the maximum value of the characteristic.

[0046] [2. Example of information processing system configuration] Next, a configuration of an information processing system 1 including a server device 100 according to an embodiment will be described with reference to Fig. 4. Fig. 4 is a diagram showing an example of the configuration of the information processing system 1 according to an embodiment. As shown in Fig. 4, the information processing system 1 according to an embodiment includes a terminal device 10 and a server device 100. These various devices are connected to each other via a network N so as to be able to communicate with each other via a wired or wireless connection. The network N is, for example, a LAN (Local Area Network) or a WAN (Wide Area Network) such as the Internet.

[0047] Furthermore, the number of devices included in the information processing system 1 shown in Fig. 4 is not limited to that shown in the figure. For example, in Fig. 4, for the sake of simplicity, only one terminal device 10 is shown, but this is merely an example and is not limiting, and two or more devices may be included.

[0048] The terminal device 10 is an information processing device used by a user U. For example, the terminal device 10 is a smart device such as a smartphone or a tablet terminal, a feature phone, a PC (Personal Computer), a PDA (Personal Digital Assistant), a game console or AV device with a communication function, a car navigation system, a wearable device such as a smart watch or a head-mounted display, smart glasses, etc.

[0049] In addition, the terminal device 10 can connect to the network N via a wireless communication network such as LTE (Long Term Evolution), 4G (4th Generation), or 5G (5th Generation: 5th generation mobile communication system), or via short-range wireless communication such as Bluetooth (registered trademark) or wireless LAN (Local Area Network), and communicate with the server device 100.

[0050] The server device 100 is, for example, a computer such as a PC or a blade server, or a mainframe or a workstation, etc. The server device 100 may be realized by cloud computing.

[0051] [3. Example of terminal device configuration] Next, the configuration of the terminal device 10 will be described with reference to Fig. 5. Fig. 5 is a diagram showing an example of the configuration of the terminal device 10. As shown in Fig. 5, 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.

[0052] (Communications Department 11) The communication unit 11 is connected to a network N (see FIG. 4) by wire or wirelessly, and transmits and receives information to and from the server device 100 via the network N. For example, the communication unit 11 is realized by a NIC (Network Interface Card), an antenna, etc.

[0053] (Display section 12) Display unit 12 is a display device that displays various information such as position information. For example, display unit 12 is a liquid crystal display (LCD) or an organic electro-luminescent display (OLED). Display unit 12 is also a touch panel display, but is not limited to this.

[0054] (Input section 13) The input unit 13 is an input device that accepts various operations from the user U. For example, the input unit 13 has buttons for inputting characters, numbers, etc. The input unit 13 may be an input / output port (I / O port), a USB (Universal Serial Bus) port, etc. If the display unit 12 is a touch panel display, a part of the display unit 12 functions as the input unit 13. The input unit 13 may be a microphone that accepts voice input from the user U. The microphone may be wireless.

[0055] (Positioning unit 14) The positioning unit 14 receives signals (radio waves) transmitted from satellites of a GPS (Global Positioning System), and acquires position information (e.g., latitude and longitude) indicating the current position of the terminal device 10, which is the device itself, based on the received signals. That is, the positioning unit 14 positions the position of the terminal device 10. Note that GPS is merely an example of a GNSS (Global Navigation Satellite System).

[0056] The positioning unit 14 can also measure the position using various methods other than GPS. For example, the positioning unit 14 may measure the position by using various communication functions of the terminal device 10 as an auxiliary positioning means for position correction, etc., as described below.

[0057] (Wi-Fi positioning) For example, the positioning unit 14 uses a Wi-Fi (registered trademark) communication function of the terminal device 10 or a communication network provided by each communication company to measure the position of the terminal device 10. Specifically, the positioning unit 14 performs Wi-Fi communication or the like and measures the distance to a nearby base station or access point, thereby measuring the position of the terminal device 10.

[0058] (Beacon positioning) The positioning unit 14 may also measure the position by using a Bluetooth (registered trademark) function of the terminal device 10. For example, the positioning unit 14 measures the position of the terminal device 10 by connecting to a beacon transmitter connected by the Bluetooth (registered trademark) function.

[0059] (geomagnetic positioning) The positioning unit 14 also measures the position of the terminal device 10 based on a geomagnetic pattern of a structure that has been measured in advance and a geomagnetic sensor that the terminal device 10 has.

[0060] (RFID positioning) Furthermore, for example, if the terminal device 10 has a function of an RFID (Radio Frequency Identification) tag equivalent to a contactless IC card used at station ticket gates, in stores, etc., or has a function of reading an RFID tag, the location where the terminal device 10 was used is recorded together with information on the payment or the like made by the terminal device 10. The positioning unit 14 may obtain such information to determine the location of the terminal device 10. Alternatively, the location may be determined by an optical sensor, an infrared sensor, or the like provided in the terminal device 10.

[0061] The positioning unit 14 may measure the position of the terminal device 10 using one or a combination of the above-mentioned positioning means, as needed.

[0062] (Sensor unit 20) The sensor unit 20 includes various sensors mounted on or connected to the terminal device 10. The connection may be wired or wireless. For example, the sensors may be detection devices other than the terminal device 10, such as wearable devices or wireless devices. In the example shown in FIG. 5, the sensor unit 20 includes an acceleration sensor 21, a gyro sensor 22, a barometric pressure sensor 23, a temperature sensor 24, a sound sensor 25, a light sensor 26, a magnetic sensor 27, and an image sensor (camera) 28.

[0063] The above-described sensors 21 to 28 are merely examples and are not intended to be limiting. That is, the sensor unit 20 may be configured to include some of the sensors 21 to 28, or may include other sensors such as a humidity sensor in addition to or instead of the sensors 21 to 28.

[0064] The acceleration sensor 21 is, for example, a three-axis acceleration sensor, and detects physical movements of the terminal device 10, such as the direction of movement, speed, and acceleration of the terminal device 10. The gyro sensor 22 detects physical movements of the terminal device 10, such as tilt in three axial directions, based on the angular velocity of the terminal device 10. The air pressure sensor 23 detects, for example, the air pressure around the terminal device 10.

[0065] Since the terminal device 10 includes the acceleration sensor 21, the gyro sensor 22, the atmospheric pressure sensor 23, etc., it is possible to measure the position of the terminal device 10 using a technique such as Pedestrian Dead-Reckoning (PDR) that uses these sensors 21 to 23. This makes it possible to obtain indoor position information that is difficult to obtain using a positioning system such as GPS.

[0066] For example, the number of steps, walking speed, and distance walked can be calculated using a pedometer that uses the acceleration sensor 21. In addition, the direction of travel, line of sight, and body tilt of the user U can be determined using the gyro sensor 22. In addition, the altitude and floor on which the terminal device 10 of the user U is located can be determined from the air pressure detected by the air pressure sensor 23.

[0067] The temperature sensor 24 detects, for example, the temperature around the terminal device 10. The sound sensor 25 detects, for example, the sound around the terminal device 10. The light sensor 26 detects the illuminance around the terminal device 10. The magnetic sensor 27 detects, for example, the geomagnetism around the terminal device 10. The image sensor 28 captures an image around the terminal device 10.

[0068] The above-mentioned air pressure sensor 23, temperature sensor 24, sound sensor 25, light sensor 26, and image sensor 28 can detect the air pressure, temperature, sound, and illuminance, respectively, and capture images of the surroundings, thereby detecting the environment and situation around the terminal device 10. Furthermore, the accuracy of the location information of the terminal device 10 can be improved based on the environment and situation around the terminal device 10.

[0069] (control unit 30) The control unit 30 includes, for example, a microcomputer having a CPU (Central Processing Unit), ROM (Read Only Memory), RAM, input / output ports, etc., and various other circuits. The control unit 30 may also be configured with hardware such as an integrated circuit, for example, an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array). The control unit 30 includes a transmitting unit 31, a receiving unit 32, and a processing unit 33.

[0070] (Transmitter 31) The transmission unit 31 can transmit, for example, various information input by the user U using the input unit 13, various information detected by each sensor 21 to 28 mounted on or connected to the terminal device 10, and location information of the terminal device 10 measured by the positioning unit 14 to the server device 100 via the communication unit 11.

[0071] (Receiver 32) The receiving unit 32 can receive various types of information provided by the server device 100 and requests for various types of information from the server device 100 via the communication unit 11.

[0072] (Processing unit 33) The processing unit 33 controls the entire terminal device 10, including the display unit 12. For example, the processing unit 33 can output various information transmitted by the transmitting unit 31 and various information received from the server device 100 by the receiving unit 32 to the display unit 12 for display.

[0073] (Storage unit 40) The storage unit 40 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory, or a storage device such as an HDD (Hard Disk Drive), an SSD (Solid State Drive), an optical disk, etc. The storage unit 40 stores various programs, various data, etc.

[0074] [4. Server device configuration example] Next, the configuration of the server device 100 according to the embodiment will be described with reference to Fig. 6. Fig. 6 is a diagram showing an example of the configuration of the server device 100 according to the embodiment. As shown in Fig. 6, the server device 100 includes a communication unit 110, a storage unit 120, and a control unit 130.

[0075] (Communication unit 110) The communication unit 110 is realized by, for example, a network interface card (NIC), etc. The communication unit 110 is also connected to a network N (see FIG. 4) by wire or wirelessly.

[0076] (Storage unit 120) The storage unit 120 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory, or a storage device such as an HDD, an SSD, an optical disk, etc. As shown in Fig. 6, the storage unit 120 has a user information database 121, a history information database 122, and a reference point information database 123.

[0077] (User Information Database 121) The user information database 121 stores user information about the user U. For example, the user information database 121 stores various information such as the attributes of the user U. FIG. 7 is a diagram showing an example of the user information database 121. In the example shown in FIG. 7, the user information database 121 has items such as "User ID (Identifier)," "Age," "Gender," "Home," "Workplace," and "Interests."

[0078] The "user ID" indicates identification information for identifying the user U. The "user ID" may be the contact information of the user U (telephone number, email address, etc.), or may be identification information for identifying the terminal device 10 of the user U.

[0079] Furthermore, "age" indicates the age of user U identified by the user ID. Note that "age" may be information indicating the specific age of user U (e.g., 35 years old), or may be information indicating the generation of user U (e.g., 30s). Alternatively, "age" may be information indicating the date of birth of user U, or may be information indicating the generation of user U (e.g., born in the 1980s). Furthermore, "gender" indicates the gender of user U identified by the user ID.

[0080] Furthermore, "home" indicates the location information of the home of user U identified by the user ID. In the example shown in FIG. 7, "home" is illustrated as an abstract code such as "LC11," but it may also be latitude and longitude information, etc. Furthermore, for example, "home" may also be the name of an area or an address.

[0081] Furthermore, "workplace" indicates location information of the workplace (school in the case of a student) of user U identified by the user ID. In the example shown in FIG. 7, "workplace" is illustrated as an abstract code such as "LC12," but it may also be latitude and longitude information, etc. Furthermore, for example, "workplace" may also be the name of a region or an address.

[0082] Furthermore, "interests" indicate the interests of user U identified by the user ID. In other words, "interests" indicate subjects in which user U identified by the user ID is highly interested. For example, "interests" may be search queries (keywords) entered by user U into a search engine. In the example shown in FIG. 7, each user U is shown with one "interest," but there may be multiple "interests."

[0083] For example, in the example shown in FIG. 7, the age of user U identified by user ID "U1" is "20s" and the gender is "male." Furthermore, for example, the home address of user U identified by user ID "U1" is "LC11." Furthermore, for example, the workplace of user U identified by user ID "U1" is "LC12." Furthermore, for example, the user U identified by user ID "U1" is interested in "sports."

[0084] 7, abstract values such as "U1", "LC11", and "LC12" are used for illustration, but "U1", "LC11", and "LC12" are assumed to store information such as specific character strings and numerical values. Below, abstract values may also be illustrated in diagrams relating to other information.

[0085] The user information database 121 may store various types of information depending on the purpose, without being limited to the above. For example, the user information database 121 may store various types of information related to the terminal device 10 of the user U. The user information database 121 may also store information related to the user U's attributes, such as demographic attributes, psychographic attributes, geographic attributes, and behavioral attributes. For example, the user information database 121 may store information such as name, family structure, hometown (hometown), occupation, job title, income, qualifications, type of residence (detached house, apartment, etc.), whether or not the user has a car, commuting time, commuting route, commuter pass area (station, line, etc.), frequently used stations (other than the station nearest to home or workplace), extracurricular activities (location, time zone, etc.), hobbies, interests, lifestyle, etc.

[0086] (History Information Database 122) The history information database 122 stores various information related to history information (log data) that indicates the behavior of the user U. Fig. 8 is a diagram showing an example of the history information database 122. In the example shown in Fig. 8, the history information database 122 has items such as "user ID," "location history," "search history," "browsing history," "purchase history," and "posting history."

[0087] "User ID" indicates identification information for identifying user U. "Location history" indicates the location history, which is the history of user U's location and movements. "Search history" indicates the search history, which is the history of search queries entered by user U. "Browsing history" indicates the browsing history, which is the history of content viewed by user U. "Purchase history" indicates the purchase history, which is the history of purchases made by user U. "Posting history" indicates the posting history, which is the history of posts made by user U. "Posting history" may also include questions about user U's possessions.

[0088] For example, in the example shown in Figure 8, user U, identified by user ID "U1," moved as shown in "Location History #1," searched as shown in "Search History #1," viewed content as shown in "Viewing History #1," purchased specific products at specific stores as shown in "Purchase History #1," and posted as shown in "Posting History."

[0089] Here, in the example shown in Figure 8, abstract values such as "U1", "Location History #1", "Search History #1", "Browsing History #1", "Purchase History #1", and "Post History #1" are used for the illustration, but "U1", "Location History #1", "Search History #1", "Browsing History #1", "Purchase History #1", and "Post History #1" are assumed to store specific information such as character strings and numbers.

[0090] The history information database 122 is not limited to the above and may store various types of information depending on the purpose. For example, the history information database 122 may store the user U's usage history of a predetermined service. The history information database 122 may also store the user U's store visit history or facility visit history. The history information database 122 may also store the user U's payment history (electronic payment) using the terminal device 10.

[0091] (Baseline Information Database 123) The reference point information database 123 stores various information relating to the characteristics of time-series changes in the input manner of search keywords. Fig. 9 is a diagram showing an example of the reference point information database 123. In the example shown in Fig. 9, the reference point information database 123 has items such as "reference point," "reference point volume," "reference point characteristic degree," "target," "target volume," "target characteristic degree," and "distance."

[0092] "Reference point" indicates identification information for identifying a reference point (starting point) set based on the ratio between the characteristic degree and volume of a reference object selected or input by user U. For example, the reference point indicates identification information for identifying a reference point plotted on a graph represented by volume and characteristic degree. Furthermore, "reference point volume" indicates the volume of the reference point. For example, the reference point volume indicates the volume value of the reference point when the vertical axis (Y axis) of a graph represented by volume and characteristic degree is taken as the volume. Furthermore, "reference point characteristic degree" indicates the characteristic degree of the reference point. For example, the reference point characteristic degree indicates the characteristic degree value of the reference point when the horizontal axis (X axis) of a graph represented by volume and characteristic degree is taken as the characteristic degree.

[0093] "Object" indicates identification information for identifying an object plotted on a graph represented by volume and characteristic degree. Also, "object volume" indicates the volume of the object. For example, object volume indicates the value of the object's volume when the vertical axis (Y axis) of a graph represented by volume and characteristic degree is taken as volume. Also, "object characteristic degree" indicates the characteristic degree of the object. For example, object characteristic degree indicates the value of the object's characteristic degree when the horizontal axis (X axis) of a graph represented by volume and characteristic degree is taken as characteristic degree.

[0094] Furthermore, "distance" indicates the distance between a reference point and an object. For example, in a graph expressed by volume and characteristic degree, distance indicates the distance between two points: the coordinate of the reference point expressed by the reference point volume and the reference point characteristic degree, and the coordinate of the object expressed by the object volume and the object characteristic degree.

[0095] For example, in the example shown in Figure 9, the distance between the coordinates of the reference point identified by reference point "P1" (reference point characteristic degree "CP1", reference point volume "VP1") and the coordinates of the object identified by object "object #1" (object characteristic degree "C#1", object volume "V#1") is shown to be "distance #1".

[0096] The reference point information database 123 is not limited to the above and may store various types of information depending on the purpose. For example, the reference point information database 123 may store identification information (user ID) for identifying the user U who set the reference point, or information indicating the cluster to which the user U belongs. The reference point information database 123 may also store identification information for identifying a reference object selected or input by the user U from among the objects plotted on the graph, information indicating the volume of the reference object, information indicating the distinctiveness of the reference object, etc.

[0097] (control unit 130) 6, the explanation will be continued. The control unit 130 is a controller, and is realized by, for example, a CPU (Central Processing Unit), an MPU (Micro Processing Unit), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or the like, executing various programs (corresponding to an example of an information processing program) stored in a storage device inside the server device 100 using a storage area such as a RAM as a working area. In the example shown in FIG. 6, the control unit 130 has an acquisition unit 131, a classification unit 132, an identification unit 133, a reception unit 134, a setting unit 135, a generation unit 136, and a provision unit 137.

[0098] (Acquisition part 131) The acquisition unit 131 acquires a search query input by the user U. For example, when the user U inputs a search query into a search engine or the like to perform a keyword search, the acquisition unit 131 acquires the search query via the communication unit 110. That is, the acquisition unit 131 acquires, via the communication unit 110, the keywords input by the user U into the search box of a search engine, website, or app.

[0099] Furthermore, the acquisition unit 131 acquires user information about the user U via the communication unit 110. For example, the acquisition unit 131 acquires identification information (such as a user ID) indicating the user U, location information of the user U, attribute information of the user U, etc. from the terminal device 10 of the user U. Furthermore, the acquisition unit 131 may acquire the identification information indicating the user U, attribute information of the user U, etc. when registering the user U. Then, the acquisition unit 131 registers the user information in the user information database 121 of the storage unit 120.

[0100] Furthermore, the acquisition unit 131 acquires various types of history information (log data) indicating the behavior of the user U via the communication unit 110. For example, the acquisition unit 131 acquires various types of history information indicating the behavior of the user U from the terminal device 10 of the user U or from various servers based on the user ID or the like. Then, the acquisition unit 131 registers the various types of history information in the history information database 122 of the storage unit 120.

[0101] (Classification section 132) The classification unit 132 further clusters users who have searched for the same search keyword based on differences in keywords of interest. The classification unit 132 also clusters users by brand based on the users' search history and purchase history. In practice, the classification unit 132 may cluster users by brand based on their store visit history or SNS posting history, in addition to or in addition to the users' search history and purchase history.

[0102] (Specific Section 133) The identification unit 133 identifies the distinctiveness and volume of a target that is related to a predetermined query and has a predetermined attribute with respect to the query. For example, the identification unit 133 determines the distinctiveness by dividing the number of times a word indicating the target is input together with the query by the number of times other words are input together with the query. In addition, the identification unit 133 may also determine the distinctiveness of a target that is related to a predetermined query and has a predetermined attribute with respect to the query. subject The number of times the word was entered, or along with the query subject The volume is the number of users who input the word indicating the above.

[0103] (Reception Department 134) The receiving unit 134 receives a selection of a reference object from a graph in which objects are plotted by volume and distinctiveness. At this time, the receiving unit 134 presents the graph in which objects are plotted by volume and distinctiveness to the user's terminal device 10 via the communication unit 110. For example, the receiving unit 134 presents the user with a graph in which objects are plotted by volume and distinctiveness, and receives an object selected by the user from among the objects plotted on the graph. Alternatively, the receiving unit 134 receives an input of an object that the user has as his or her hypothesis.

[0104] (Setting section 135) The setting unit 135 sets a reference point based on the ratio between the characteristic score and the volume of the selected reference object. For example, the setting unit 135 calculates the ratio between the characteristic score and the volume of the selected reference object, and sets a reference point on the reference line where the ratio between the volume and the characteristic score is the same as the calculated ratio.

[0105] Furthermore, when calculating the distance between the set reference point and the position where each object is plotted, the setting unit 135 changes the reference point based on the ratio between the characteristic level and the volume of the selected reference object. Alternatively, when calculating the distance between the set reference point and the position where each object is plotted, the setting unit 135 weights the reference point or the distance based on the ratio between the characteristic level and the volume of the selected reference object.

[0106] The setting unit 135 also functions as a ratio calculation unit that calculates the ratio between the set reference point and the characteristic level and volume of the selected reference object, and a distance calculation unit that calculates the distance between the set reference point and the position where each object is plotted. For example, the setting unit 135 includes a ratio calculation unit that calculates the ratio between the set reference point and the characteristic level and volume of the selected reference object, and a distance calculation unit that calculates the distance between the set reference point and the position where each object is plotted.

[0107] (Generation unit 136) The generation unit 136 generates a ranking in which the objects are arranged in descending order of distance between the set reference point and the position at which each object is plotted. At this time, the generation unit 136 generates information related to the ranking in which the objects are arranged in descending order of distance between the set reference point and the position at which each object is plotted. For example, the generation unit 136 generates an appropriate list of volumes and distinctiveness (a list of volumes x distinctiveness) as information related to the ranking.

[0108] (Providing Department 137) The providing unit 137 provides information about the ranking in which the objects are arranged in descending order of distance between the set reference point and the position where each object is plotted, to the user's terminal device 10 via the communication unit 110. For example, the providing unit 137 provides an appropriate list of volumes and distinctiveness (a list of volume x distinctiveness) as the information about the ranking.

[0109] [5. Processing Procedure] Next, a processing procedure by the server device 100 according to the embodiment will be described with reference to Fig. 10. Fig. 10 is a flowchart showing the processing procedure according to the embodiment. Note that the processing procedure shown below is repeatedly executed by the control unit 130 of the server device 100.

[0110] 10, the acquisition unit 131 of the server device 100 receives input of search keywords from the terminal device 10 of each user U via the communication unit 110 (step S101). That is, the acquisition unit 131 collects the search keywords via the communication unit 110. Note that the acquisition unit 131 may also acquire attribute information, history information, etc. of each user U from an external server device via the communication unit 110.

[0111] Next, the classification unit 132 of the server device 100 extracts a target person from the search history of each user U (step S102).

[0112] Next, the classification unit 132 of the server device 100 extracts keywords of interest to the target person (step S103).

[0113] Next, the classification unit 132 of the server device 100 clusters (groups) the subjects based on their search tendencies (step S104). For example, the classification unit 132 classifies the subjects into a plurality of clusters based on keywords of interest.

[0114] Next, the identifying unit 133 of the server device 100 identifies the volume of the target that is related to the predetermined query and has a predetermined attribute (step S105). For example, the identifying unit 133 identifies the characteristic degree and volume of the target for each cluster of the target. Note that the target may be a target classified into multiple clusters.

[0115] Next, the specifying unit 133 of the server device 100 specifies the characteristic degree of the object that is related to the predetermined query and has a predetermined attribute (step S106).

[0116] Next, the receiving unit 134 of the server device 100 receives the selection of a reference object from the graph in which the objects are plotted by volume and characteristic degree (step S107).

[0117] Next, the setting unit 135 of the server device 100 sets a reference point based on the ratio between the characteristic degree and the volume of the selected reference object (step S108).

[0118] Next, the generating unit 136 of the server device 100 generates a ranking in which the objects are arranged in order of the distance between the set reference point and the position where each object is plotted (step S109).

[0119] Next, the providing unit 137 of the server device 100 provides, via the communication unit 110, to the user's terminal device 10, information regarding a ranking in which the objects are arranged in order of the distance between the set reference point and the position where each object is plotted (step S110).

[0120] [6. Modifications] The terminal device 10 and the server device 100 described above may be implemented in various different forms other than the above embodiment. Therefore, modifications of the embodiment will be described below.

[0121] In the above embodiment, some or all of the processing executed by the server device 100 may actually be executed by the terminal device 10. For example, the processing may be completed in a stand-alone manner (by the terminal device 10 alone). In this case, the terminal device 10 is assumed to have the functions of the server device 100 in the above embodiment. Furthermore, in the above embodiment, the terminal device 10 is linked to the server device 100, and therefore, from the perspective of the user U, it appears that the processing of the server device 100 is also being executed by the terminal device 10. In other words, from another perspective, the terminal device 10 can also be said to be equipped with the server device 100.

[0122] In the above embodiment, the server device 100 extracts subjects who have searched for the same search keyword and then clusters the subjects based on the keywords of interest of the subjects. However, in practice, it is not necessary to extract subjects. For example, the server device 100 may cluster an unspecified number of user groups based on the keywords of interest of the unspecified number of user groups.

[0123] In the above embodiment, the server device 100 may change the display mode of a reference object selected or input by a user among the objects plotted on a graph in which the objects are plotted by volume and distinctiveness, or the display mode of a set reference point. For example, the server device 100 may change the color, pattern, shape, etc. of the reference object selected or input by a user or the set reference point, or may highlight it by emitting light, blinking, or by using a thick frame, etc.

[0124] In the above embodiment, the server device 100 may display, in a graph in which objects are plotted by volume and distinctiveness, the coordinates of a reference object selected or input by the user or a set reference point among the objects plotted on the graph, as numerical values. For example, the server device 100 may display numerical values indicating the coordinates near the reference object selected or input by the user or the set reference point, or in a speech bubble or the like.

[0125] In the above embodiment, the server device 100 may display a ranking of the objects plotted on a graph in which the objects are plotted by volume and distinctiveness. For example, the server device 100 may display a numerical value indicating the ranking near the object plotted on the graph or in a speech bubble or the like.

[0126] [7. Effects] As described above, the information processing device (terminal device 10 and server device 100) according to the present application includes an identification unit 133 that identifies the distinctiveness and volume of a target that is related to a predetermined query and has a predetermined attribute, a reception unit 134 that receives a selection of a reference target from a graph in which targets are plotted in terms of volume and distinctiveness, a setting unit 135 that sets a reference point based on the ratio of the distinctiveness and volume of the selected reference target, a generation unit 136 that generates a ranking in which targets are arranged in order of proximity from the set reference point to the position at which each target is plotted, and a provision unit 137 that provides information related to the ranking.

[0127] The identification unit 133 determines the characteristic score by dividing the number of times the word indicating the target was input together with the query by the number of times other words were input together with the query.

[0128] The identification unit 133 receives the query and subject The number of times the word was entered, or along with the query subject The volume is the number of users who input the word indicating the above.

[0129] The receiving unit 134 presents a graph in which objects are plotted in terms of volume and characteristic degree to the user, and receives an object selected by the user from among the objects plotted on the graph.

[0130] The receiving unit 134 receives an input of a target that the user has as his or her hypothesis.

[0131] The setting unit 135 calculates the ratio between the characteristic score and the volume of the selected reference object, and sets a reference point on the reference line where the ratio between the volume and the characteristic score is the same as the calculated ratio.

[0132] When calculating the distance between the set reference point and the position where each object is plotted, the setting unit 135 changes the reference point based on the ratio between the characteristic degree and the volume of the selected reference object.

[0133] When calculating the distance between the set reference point and the position where each object is plotted, the setting unit 135 weights the reference point or the distance based on the ratio of the distinctiveness and volume of the selected reference object.

[0134] By performing any one or a combination of the above processes, the information processing device according to the present application can create an appropriate list of volumes and characteristic degrees.

[0135] [8. Hardware Configuration] The terminal device 10 and the server device 100 according to the above-described embodiments are realized by a computer 1000 having a configuration as shown in Fig. 11, for example. The following description will be given taking the server device 100 as an example. Fig. 11 is a diagram showing an example of a hardware configuration. The computer 1000 is connected to an output device 1010 and an input device 1020, and has a configuration in which a calculation device 1030, a primary storage device 1040, a secondary storage device 1050, an output I / F (Interface) 1060, an input I / F 1070, and a network I / F 1080 are connected via a bus 1090.

[0136] The arithmetic device 1030 operates based on programs stored in the primary storage device 1040 and the secondary storage device 1050, programs read from the input device 1020, and the like, and executes various processes. The arithmetic device 1030 is realized by, for example, a CPU (Central Processing Unit), an MPU (Micro Processing Unit), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array), or the like.

[0137] The primary storage device 1040 is a memory device such as a RAM (Random Access Memory) that temporarily stores data used by the arithmetic device 1030 for various calculations. The secondary storage device 1050 is a storage device in which data used by the arithmetic device 1030 for various calculations and various databases are registered, and is realized by a ROM (Read Only Memory), an HDD (Hard Disk Drive), an SSD (Solid State Drive), a flash memory, or the like. The secondary storage device 1050 may be an internal storage device or an external storage device. The secondary storage device 1050 may also be a removable storage medium such as a USB (Universal Serial Bus) memory or an SD (Secure Digital) memory card. The secondary storage device 1050 may also be cloud storage (online storage), a NAS (Network Attached Storage), a file server, or the like.

[0138] The output I / F 1060 is an interface for transmitting information to be output to an output device 1010 that outputs various types of information, such as a display, a projector, a printer, etc., and is realized by a connector conforming to a standard such as USB (Universal Serial Bus), DVI (Digital Visual Interface), or HDMI (High Definition Multimedia Interface), etc. The input I / F 1070 is an interface for receiving information from various input devices 1020, such as a mouse, a keyboard, a keypad, a button, a scanner, etc., and is realized by a USB, etc.

[0139] Furthermore, 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.

[0140] The output device 1010 and the input device 1020 may be integrated into one device, such as a touch panel. In this case, the output I / F 1060 and the input I / F 1070 may also be integrated into one device as an input / output I / F.

[0141] The input device 1020 may be a device that reads information from, for example, an optical recording medium such as a CD (Compact Disc), a DVD (Digital Versatile Disc), or a PD (Phase Change Rewritable Disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory.

[0142] The network I / F 1080 receives data from other devices via the network N and sends it to the arithmetic device 1030, and also transmits data generated by the arithmetic device 1030 to other devices via the network N.

[0143] The arithmetic unit 1030 controls the output device 1010 and the input device 1020 via the output I / F 1060 and the input I / F 1070. For example, the arithmetic unit 1030 loads a program from the input device 1020 or the secondary storage device 1050 onto the primary storage device 1040 and executes the loaded program.

[0144] For example, when the computer 1000 functions as the server device 100, the arithmetic unit 1030 of the computer 1000 executes a program loaded onto the primary storage device 1040 to realize the functions of the control unit 130. The arithmetic unit 1030 of the computer 1000 may also load a program acquired from another device via the network I / F 1080 onto the primary storage device 1040 and execute the loaded program. The arithmetic unit 1030 of the computer 1000 may also cooperate with the other device via the network I / F 1080 to call and use the functions and data of a program from another program of the other device.

[0145] [9. Other] Although the embodiments of the present application have been described above, the present invention is not limited to the contents of these embodiments. Furthermore, the above-described components include those that can be easily imagined by a person skilled in the art, those that are substantially the same, and those that are within the scope of so-called equivalents. Furthermore, the above-described components can be combined as appropriate. Furthermore, various omissions, substitutions, or modifications of the components can be made without departing from the spirit of the above-described embodiments.

[0146] Furthermore, among the processes described in the above embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically using a known method. In addition, the information including the processing procedures, specific names, various data, and parameters shown in the above documents and drawings can be changed as desired unless otherwise specified. For example, the various information shown in each drawing is not limited to the information shown in the drawings.

[0147] Furthermore, the components of each device shown in the figure are conceptual functional components and do not necessarily have to be physically configured as shown in the figure. In other words, the specific form of distribution and integration of each device is not limited to that shown in the figure, and all or part of them can be functionally or physically distributed and integrated in any unit depending on various loads, usage conditions, etc.

[0148] For example, the above-mentioned server device 100 may be realized by multiple server computers, and depending on the function, the configuration can be flexibly changed, such as by calling an external platform using an API (Application Programming Interface) or network computing.

[0149] Furthermore, the above-described embodiments and modifications can be combined as appropriate within the scope of not causing any contradiction in the processing content.

[0150] Furthermore, the above-mentioned "section, module, unit" can be read as "means" or "circuit," etc. For example, an acquisition unit can be read as an acquisition means or an acquisition circuit. [Explanation of symbols]

[0151] 1. Information Processing Systems 10 Terminal Equipment 100 Server device 110 Communications Department 120 Storage section 121 User Information Database 122 Historical Information Database 123 Control Point Information Database 130 Control Unit 131 Acquisition Department 132 Classification Department 133 Specific part 134 Reception Department 135 Settings 136 Generation part 137 Provision Department

Claims

1. an identification unit that identifies a distinctiveness and a volume of an object that is related to a predetermined query and has a predetermined attribute; a receiving unit that receives a selection of a reference object from a graph in which objects are plotted by volume and distinctiveness; a setting unit that sets a reference point based on the ratio of the characteristic degree and the volume of the selected reference object; a generating unit that generates a ranking in which the objects are arranged in order of the distance between the set reference point and the position where each object is plotted; a providing unit that provides information about the ranking; Equipped with The identification unit The distinctiveness is determined by dividing the number of times the word indicating the target is entered together with the query by the number of times other words are entered together with the query; The number of times a word indicating the target is entered along with the query, or the number of users who entered a word indicating the target along with the query, is identified as volume.

1. An information processing device comprising:

2. The identification unit determines the distinctiveness by dividing the number of times the user who input the query converted the target by the number of times the user converted another target.

2. The information processing apparatus according to claim 1, wherein:

3. The identification unit determines the number of users who have input a query and converted the target as the volume.

3. The information processing apparatus according to claim 1, wherein the information processing apparatus is a computer.

4. The receiving unit presents a graph in which objects are plotted by volume and characteristic degree to a user, and receives an object selected by the user from among the objects plotted on the graph.

4. The information processing device according to claim 1, wherein the information processing device is a computer.

5. The receiving unit receives an input of an object that the user estimates as a reference object in his / her hypothesis.

5. The information processing device according to claim 1, wherein the information processing device is a computer.

6. The setting unit Calculating the ratio of the distinctiveness and volume of the selected reference object; A reference point is set on the reference line where the ratio of the volume to the characteristic degree is the same as the calculated ratio.

6. The information processing device according to claim 1, wherein the information processing device is a computer.

7. The setting unit changes the reference point based on the ratio of the characteristic degree and the volume of the selected reference object when calculating the distance between the set reference point and the position where each object is plotted.

7. The information processing device according to claim 1, wherein the information processing device is a computer.

8. When calculating the distance between the set reference point and the position where each object is plotted, the setting unit weights the reference point or the distance based on the ratio of the characteristic degree and the volume of the selected reference object.

8. The information processing device according to claim 1, wherein the information processing device is a computer.

9. An information processing method executed by an information processing device, an identifying step of identifying a distinctiveness and volume of an object having a relationship with a predetermined query, the object having a predetermined attribute; a receiving step of receiving a selection of a reference object from a graph in which the objects are plotted by volume and distinctiveness; a setting step of setting a reference point based on the ratio of the characteristic degree and the volume of the selected reference object; a generating step of generating a ranking in which the objects are arranged in order of the distance between the set reference point and the position where each object is plotted; a providing step of providing information about the ranking; Including, In the specifying step, The distinctiveness is determined by dividing the number of times the word indicating the target is entered together with the query by the number of times other words are entered together with the query; The number of times a word indicating the target is entered along with the query, or the number of users who entered a word indicating the target along with the query, is identified as volume. An information processing method comprising:

10. an identification step of identifying a distinctiveness and a volume of an object having a relationship with a predetermined query, the object having a predetermined attribute; a receiving step for receiving a selection of a reference object from a graph plotting the objects by volume and distinctiveness; a setting step of setting a reference point based on the ratio of the distinctiveness and volume of the selected reference object; a generation procedure for generating a ranking in which the objects are arranged in order of the distance between the set reference point and the position where each object is plotted; a provision step of providing information about the ranking; An information processing program for causing a computer to execute the above, In the identification step, The distinctiveness is determined by dividing the number of times the word indicating the target is entered together with the query by the number of times other words are entered together with the query; The number of times a word indicating the target is entered along with the query, or the number of users who entered a word indicating the target along with the query, is identified as volume. An information processing program characterized by:

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