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

Abstract

To provide an information processing apparatus, an information processing method, and an information processing program that can evaluate users whose attribute information is to be estimated.SOLUTION: An information processing apparatus according to the present application comprises: an acquisition unit that acquires user information indicating information on users and action information including users' search histories; a model storage unit that stores a plurality of models that have learned the relationship between the user information, action information, and specific characteristics of users; a calculation unit that calculates indices for the specific characteristics for every user by using the plurality of models; a questioning unit that asks a question related to the specific characteristics to the users for whom the calculation unit has calculated the indices for the specific characteristics; and an evaluation unit that, based on answers from the users to the question asked by the questioning unit and the indices for the specific characteristics calculated for the users, evaluates the degree of properness of the answers from the users.SELECTED DRAWING: Figure 3

Description

[Technical Field]

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

[0002] Attribute information indicating the attributes of users to whom information is to be delivered is registered in advance as delivery destination information, and targeted delivery is carried out in which advertisements corresponding to the attribute information of the users to whom information is to be delivered are selectively delivered.

[0003] For example, Patent Document 1 below discloses a technology for calculating the true relevance between an advertisement and attribute information based on the apparent degree of relevance between the advertisement and attribute information and the average degree of relevance between multiple advertisements and attribute information. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2015-1956 Summary of the Invention [Problem to be solved by the invention]

[0005] When estimating a user's attribute information in this way, it is sometimes necessary to verify the accuracy of the estimation by asking the user questions. In such cases, since users do not always answer honestly, it has been necessary to evaluate the users who have been asked questions.

[0006] In view of the above-described problems, the present disclosure aims to provide an information processing device, an information processing method, and an information processing program capable of evaluating a user whose attribute information is to be estimated. [Means for solving the problem]

[0007] In order to solve the above-mentioned problems and achieve the objectives, the information processing device according to the present disclosure includes an acquisition unit that acquires user information indicating information about a user and behavioral information including the user's search history, a model storage unit that stores a plurality of models that have been trained to learn the relationship between the user information, the behavioral information, and specific characteristics of the user, a calculation unit that calculates an index for the specific characteristic for each user using the plurality of models, a questioning unit that asks questions related to the specific characteristic to users for whom the calculation unit has calculated an index for the specific characteristic, and an evaluation unit that evaluates the appropriateness of the user's answer based on the user's answer to the question asked by the questioning unit and the index for the specific characteristic calculated for the user. [Effects of the Invention]

[0008] According to one aspect of the embodiment, it is possible to provide an information processing device, an information processing method, and an information processing program capable of evaluating a user whose attribute information is to be estimated. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a diagram illustrating an example of information processing according to an embodiment. [Figure 2] FIG. 2 is a diagram illustrating an example of the configuration of an information processing system according to the embodiment. [Figure 3] FIG. 3 is a diagram illustrating an example of the configuration of the information processing device according to the embodiment. [Figure 4] FIG. 4 is a diagram illustrating an example of information stored in the user information storage unit of the information processing device according to the embodiment. [Figure 5] FIG. 5 is a diagram illustrating an example of information stored in the behavior information storage unit of the information processing device according to the embodiment. [Figure 6] FIG. 6 is a diagram illustrating an example of information stored in the model storage unit of the information processing apparatus according to the embodiment. [Figure 7] FIG. 7 is a diagram illustrating an example of the configuration of a user terminal according to the embodiment. [Figure 8]FIG. 8 is a flowchart illustrating an example of information processing according to the embodiment. [Figure 9] FIG. 9 is a hardware configuration diagram illustrating an example of a computer that realizes the functions of the information processing device. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, the information processing device, the information processing method, and the information processing program according to the present application 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 the embodiments.

[0011] (Embodiment) 1-1. Example of information processing according to the embodiment First, an example of information processing according to an embodiment will be described with reference to Fig. 1. Fig. 1 is a diagram showing an example of information processing according to an embodiment. First, an overview of the information processing according to the embodiment will be described, and then each process will be described in detail.

[0012] 1, an information processing device 100 acquires user information and behavioral information from multiple user terminals 200, inputs the acquired user information and behavioral information into multiple models, calculates an index for a specific feature, asks the user questions related to the specific feature, checks whether there is a discrepancy between the index for the specific feature and the answers to the questions, and evaluates the user based on whether there is a discrepancy. Below, an example of information processing according to an embodiment will be described in detail step by step using FIG. 1.

[0013] First, the information processing device 100 acquires user information and behavioral information from multiple user terminals 200 (step S1). For example, as shown in Fig. 1, the information processing device 100 acquires user information and behavioral information from user terminals 200A to 200F of users U1 to U6. Note that users U1 to U6 and user terminals 200A to 200F shown in Fig. 1 are examples, and the information processing device 100 may acquire user information and behavioral information from user terminals 200 of more users.

[0014] Next, the information processing device 100 calculates an index for the specific feature using multiple models (step S2). Note that the model here refers to a model that has learned the relationship between user information, behavioral information, and an index related to the user's specific feature. When the user information and behavioral information of the user are input, the model outputs an index related to the user's specific feature as a value between 0 and 1. The specific feature can also be referred to as user attribute information, and is a concept that indicates the user's hobbies, preferences, and characteristics, such as "I like glasses" or "I like books." The information processing device 100 inputs the user information and behavioral information acquired from multiple user terminals 200 into multiple models that calculate an index for the specific feature, and calculates an index for the user's specific feature for each model.

[0015] Next, the information processing device 100 transmits question information related to the specific characteristic to the plurality of user terminals 200 (step S3). For example, the information processing device 100 may transmit question information, which asks a multiple-choice or input-type question as to whether or not the specific characteristic "likes glasses" applies to the user terminals 200 of the plurality of users for whom the index related to the specific characteristic was calculated in step S2, and the plurality of user terminals 200 that have received the question information may display the question information on the output unit 230, thereby asking the users for whom the index related to the characteristic was calculated in step S3 whether or not the specific characteristic applies to them.

[0016] Next, the information processing device 100 receives answer information to a question related to the specific feature from the multiple user terminals 200 (step S4). For example, the information processing device 100 may receive answer information from the user terminal 200 that a user U inputs via the input unit 220 of the user terminal 200 after checking a question related to the specific feature displayed on the user terminal 200. In this case, the answer information that the user U inputs into the input unit 220 of the user terminal 200 may be an answer to the question expressed as "yes" or "no," or an answer to the question expressed in sentences. As shown in FIG. 1, the information processing device 100 receives answer information input by the user U from the multiple user terminals 200.

[0017] Next, the information processing device 100 checks whether there is a discrepancy between the calculated index for the specific feature of the user and the user's answer (step S5). For example, assume that the information processing device 100 calculates an index for a specific feature of user U1, such as "liking glasses," as 0.8. In this case, assume that the information processing device 100 sends a question related to the specific feature, "Do you like glasses?" to the user terminal 200A of user U1, and user U1 answers "yes." Here, it is estimated that the higher the index value, the more likely the user is to have the specific feature. Therefore, user U1's answer "yes" is considered appropriate. In this case, the information processing device 100 determines that there is no discrepancy between user U1's index and answer.

[0018] Next, the information processing device 100 evaluates the users based on whether or not there is a discrepancy (step S6). If the index of user U1 is calculated as 0.8 and user U1 answers "yes," the information processing device 100 will evaluate the user highly. In this case, for example, the information processing device 100 may express the evaluation of user U1 as a number in the range of 0 to 1, and may evaluate user U1 as 0.8, for example.

[0019] This allows the information processing device 100 to evaluate the user based on whether there is a discrepancy between the calculation result of the index related to the specific feature of the user and the answer to the question related to the specific feature of the user. Therefore, it is possible to provide the information processing device 100 that can evaluate the user whose attribute information is to be estimated.

[0020] 1-2. Another example of information processing according to an embodiment 1 The information processing device 100 asks the user a multiple-choice question about whether or not the user has the specific characteristic, as a question related to the specific characteristic.

[0021] This information processing will be explained step by step. First, the information processing device 100 executes the same processing as steps S1 to S2 shown in Fig. 1. The processing from step S1 to step S2 is the same as the processing described above, and therefore the explanation will be omitted.

[0022] Next, the information processing device 100 transmits question information related to the specific characteristic to the multiple user terminals 200 (step S3). In this case, the information processing device 100 transmits question information related to the specific characteristic, including a multiple-choice question as to whether or not the user has the specific characteristic, to the user terminal 200. For example, the information processing device 100 may transmit question information including a question such as "Do you like glasses? Please answer 'yes' or 'no'" to the user terminals 200 of the multiple users for whom the index related to the specific characteristic was calculated in step S2, and the multiple user terminals 200 that receive the question information may display the question and a selection box for 'yes' and 'no' on the output unit 230, thereby asking the users for whom the index related to the characteristic was calculated in step S3 whether or not they have the specific characteristic.

[0023] Next, the information processing device 100 executes the same processes as those from step S4 to step S6 shown in Fig. 1. The processes from step S4 to step S6 are the same as those described above, and therefore description thereof will be omitted.

[0024] This allows the user to be asked closed questions about specific characteristics, which prevents the user from giving ambiguous answers, making it possible to provide the information processing device 100 that can accurately evaluate users whose attribute information is to be estimated.

[0025] 1-3. Another example of information processing according to the embodiment 2 The information processing device 100 asks the user questions for each specific feature category, evaluates the user for each specific feature category, and outputs the user's evaluation for each specific feature category.

[0026] This information processing will be explained step by step. First, the information processing device 100 executes the same processing as steps S1 to S2 shown in Fig. 1. The processing from step S1 to step S2 is the same as the processing described above, and therefore the explanation will be omitted.

[0027] Next, the information processing device 100 transmits question information related to the specific feature to the multiple user terminals 200 (step S3). In this case, the information processing device 100 asks a question to the user for each classification of the specific feature. Note that the classification of the specific feature here refers to the name of a classification into which the specific feature is divided, and may be, for example, "food," "sports," or "movies." For example, the information processing device 100 may transmit question information including a question such as "Do you like bananas? Please answer 'yes' or 'no'" to the user terminals 200 of the multiple users for whom the index for the specific feature was calculated in step S2 for each classification of the specific feature. In the case of a specific feature belonging to the "food" category, for example, the information processing device 100 may then transmit question information including a question such as "Do you like bananas? Please answer 'yes' or 'no'." The multiple user terminals 200 that receive the question information may then display the question text and "yes' and "no'" selection boxes on the output unit 230, thereby asking the users for whom the index for the feature was calculated in step S3 whether the specific feature applies to them.

[0028] Next, the information processing device 100 executes the same process as step S4 shown in Fig. 1. The process of step S4 is the same as the process described above, and therefore a description thereof will be omitted.

[0029] Next, the information processing device 100 checks whether there is a discrepancy between the index calculation results and the answer (step S5). In this case, the information processing device 100 checks whether there is a discrepancy between the index calculation results and the answer for each classification of the specific feature. For example, the information processing device 100 checks whether there is a discrepancy between the calculation results of the index for the specific feature and the user's answer for each classification such as "food" or "sports." Specifically, if the index for the user's specific feature is calculated to be a predetermined value or more, for example, a value of 0.6 or more, but the user answers "No," it is determined that there is a discrepancy between the index for the specific feature and the user's answer. Note that the predetermined value for the index for the specific feature in this case may be set to any value.

[0030] Next, the information processing device 100 evaluates the user based on whether or not there is a discrepancy (step S6). In this case, the information processing device 100 evaluates the user for each category of specific features and outputs the user's evaluation for each category of specific features. For example, the information processing device 100 evaluates the user for each category of specific features based on whether or not there is a discrepancy between the calculated results of indices for specific features related to "gourmet" and "sports" and the user's answers. That is, the information processing device 100 comprehensively evaluates whether or not there is a discrepancy between the indices for multiple specific features related to "gourmet" of the user and the user's answers regarding those features. For example, the information processing device 100 may calculate an evaluation value for each specific feature based on whether or not there is a discrepancy between the indices for multiple specific features related to "gourmet" and the user's answers, and then calculate an average value for the multiple specific features belonging to the "gourmet" category, thereby evaluating the user for each category of specific features.

[0031] This allows the information processing device 100 to evaluate the user for each specific feature category, thereby providing the information processing device 100 that can evaluate the user whose attribute information is to be estimated.

[0032] 1-4. Another example 3 of information processing according to the embodiment The information processing device 100 suggests a method for improving the user's evaluation from the viewpoint of how to ask questions and categorizing specific features.

[0033] This information processing will be explained step by step. First, the information processing device 100 executes the same processing as steps S1 to S2 shown in Fig. 1. The processing from step S1 to step S2 is the same as the processing described above, and therefore the explanation will be omitted.

[0034] Next, the information processing device 100 transmits question information related to the specific feature to the plurality of user terminals 200 (step S3). In this case, the information processing device 100 asks the user multiple-choice or input questions for each classification of the specific feature. The processing of the information processing device 100 in this case is the same as the processing described above, and therefore a description thereof will be omitted.

[0035] Next, the information processing device 100 executes the same processes as those from step S4 to step S6 shown in Fig. 1. The processes from step S4 to step S6 are the same as those described above, and therefore description thereof will be omitted.

[0036] Next, the information processing device 100 proposes a method for improving the evaluation of the user who made the evaluation from the perspective of the way the question is asked and the classification of the specific feature (step S7). For example, suppose that in step S3, it is recognized that user U1's evaluations tend to be higher when multiple-choice questions are asked compared to when input-type questions are asked. Furthermore, suppose that in step S6, it is recognized that the evaluations of answers to questions about "gourmet" as a classification of specific feature by user U1 tend to be significantly higher compared to other classifications of specific feature. In this case, the information processing device 100 proposes to ask user U1 multiple-choice questions and multiple-choice questions about "gourmet" as a classification of specific feature. That is, the information processing device 100 analyzes the user's evaluation trends from the perspective of the way the user asks the question and the classification of the specific feature, and proposes a way of asking the question and a classification of the specific feature that are likely to improve the user's evaluation.

[0037] This allows the information processing device 100 to obtain a method for asking questions to the user and a method for improving the evaluation of the user from the viewpoint of classification of specific features, thereby providing the information processing device 100 capable of evaluating the user whose attribute information is to be estimated.

[0038] [2. Information Processing System Configuration] Next, the configuration of an information processing system according to an embodiment will be described with reference to Fig. 2. Fig. 2 is a diagram showing an example of the configuration of an information processing system according to an embodiment. As shown in Fig. 2, an information processing system 1 includes an information processing device 100 and a user terminal 200. Note that the information processing system 1 shown in Fig. 2 may be configured to include a plurality of information processing devices 100 and a plurality of user terminals 200. The information processing device 100 and the user terminal 200 are connected to each other via a predetermined communication network (network N) so as to be able to communicate with each other via wired or wireless communication.

[0039] The information processing device 100 may be, for example, a personal computer (PC), a workstation (WS), a computer with server functions, etc. The information processing device 100 performs processing based on information transmitted from the user terminal 200 via the network N.

[0040] The user terminal 200 is an information processing device used by a user. The user terminal 200 may be, for example, an information processing device such as a smartphone, a tablet terminal, a desktop PC, a notebook PC, a mobile phone, or a PDA (Personal Digital Assistant). In the example shown in FIG. 1, the user terminal 200 is a smartphone.

[0041] 3. Configuration of Information Processing Device Next, the configuration of the information processing device 100 according to the embodiment will be described with reference to Fig. 3. Fig. 3 is a diagram showing an example of the configuration of the information processing device according to the embodiment.

[0042] 3, the information processing device 100 includes a communication unit 110, a storage unit 120, and a control unit 130. Although not shown in FIG. 3, the information processing device 100 may also include an input unit (e.g., a keyboard, a mouse, etc.) that accepts various operations from an administrator of the information processing device 100, and a display unit (e.g., a liquid crystal display, etc.) that displays various information.

[0043] (Regarding the communication unit 110) The communication unit 110 is realized by, for example, a network interface card (NIC), etc. The communication unit 110 is connected to the network N by wire or wirelessly, and transmits and receives information to and from the user terminal 200.

[0044] (Regarding the storage unit 120) The storage unit 120 is realized by, for example, a semiconductor memory element such as a random access memory (RAM) or a flash memory, or a storage device such as a hard disk, a solid state drive (SSD), an optical disk, etc. As shown in FIG. 3 , the storage unit 120 has a user information storage unit 121, a behavior information storage unit 122, and a model storage unit 123.

[0045] (Regarding the user information storage unit 121) The user information storage unit 121 stores information about a user, i.e., user information. The user information is, for example, information about a user that is provided to an information service provider when the user uses a predetermined information service. Here, an example of information stored in the user information storage unit 121 will be described with reference to FIG. 4. FIG. 4 is a diagram showing an example of information stored in the user information storage unit of the information processing device according to the embodiment.

[0046] In the example shown in FIG. 4, the user information storage unit 121 stores information relating to the items "user ID," "date of birth," "gender," and "occupation" in association with each other.

[0047] "User ID" is an identifier that identifies a user and is represented by a string of characters or a number. "Date of birth" is information about the user's date of birth linked to the "User ID". "Gender" is information about the user's gender linked to the "User ID". "Occupation" is information about the user's occupation linked to the "User ID".

[0048] That is, FIG. 4 shows that the date of birth of the user identified by the user ID "UID#1" is "date of birth #U1", the gender is "female", and the occupation is "occupation #U1".

[0049] The information stored in the user information storage unit 121 is not limited to information relating to the items "user ID," "date of birth," "gender," and "occupation," but may also store any other information related to the user.

[0050] (Regarding the behavioral information storage unit 122) The behavioral information storage unit 122 stores information indicating the behavior of a user, i.e., behavioral information. The behavioral information is information indicating the behavior of a user using a predetermined information service, which is generated as a result of the user's use of the predetermined information service. Here, an example of information stored in the behavioral information storage unit 122 will be described with reference to FIG. 5. FIG. 5 is a diagram showing an example of information stored in the behavioral information storage unit of the information processing device according to the embodiment.

[0051] In the example shown in FIG. 5, the behavior information storage unit 122 stores information relating to the items "user ID," "search history," "browsing history," "purchase history," "route search history," and "bulletin board posting history" in association with each other.

[0052] "User ID" is an identifier that identifies a user and is represented by a string of characters, a number, etc. "Search history" is information that includes the search query used by a user linked to a "User ID" for a search and the time of entry. "Browsing history" is information that includes the sites viewed by a user linked to a "User ID" and the time of viewing. "Purchase history" is information that includes the products or services purchased by a user linked to a "User ID" on an internet mail order site or a specified service contract site and the time of purchase. "Route search history" is information that includes the route search results and the search time of a user linked to a "User ID". "Bulletin board posting history" is information that includes the bulletin board posts by a user linked to a "User ID" and the time of posting.

[0053] That is, in Figure 5, the search history of the user identified by the user ID "UID#1" is "Search History #U1", the user's browsing history is "Browse History #U1", the purchase history is "Purchase History #U1", the route search history is "Route Search History #U1", and the message board posting history is "Message board posting history #U1".

[0054] The information stored in the behavioral information storage unit 122 is not limited to information relating to the items "user ID," "search history," "browsing history," "purchase history," "route search history," and "bulletin board posting history," but may also store any other information relating to the behavior of the user.

[0055] (Regarding the model storage unit 123) The model storage unit 123 stores a plurality of models that have been trained on the relationship between user information, behavioral information, and specific characteristics of users. When user information and behavioral information are input to the model, an index for the specific characteristic is calculated. Fig. 6 is a diagram showing an example of information stored in the model storage unit of the information processing device according to the embodiment.

[0056] In the example shown in FIG. 6, the model storage unit 123 stores information relating to the items "model ID" and "model data" in association with each other.

[0057] "Model ID" is an identifier that identifies a machine learning model and is represented by a character string, a number, or the like. "Model data" indicates the model data of the machine learning model. For example, the machine learning model may be a neural network.

[0058] 6, the model identified by the model ID "M#1" indicates the machine learning model M#1. Also, model data "MDT#1" indicates the model data of the machine learning model M#1.

[0059] Here, if the machine learning model is a neural network, the model data "MDT#1" includes various information, such as connection information on how the nodes included in each of the multiple layers that make up the neural network are connected to each other, and connection coefficients that are multiplied by the numerical values ​​input and output between the connected nodes.

[0060] The model storage unit 123 is not limited to storing information related to the items "model ID" and "model data," and may store information related to any other machine learning model.

[0061] (Regarding the control unit 130) Next, returning to Fig. 3, the control unit 130 will be described. The control unit 130 is realized by a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or the like executing various programs stored in a storage device of the information processing device 100 using RAM as a work area. The control unit 130 may also be realized by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array), for example.

[0062] As shown in FIG. 3, the control unit 130 includes an acquisition unit 131, a calculation unit 132, a question unit 133, a confirmation unit 134, an evaluation unit 135, and a proposal unit 136.

[0063] (Regarding the acquisition unit 131) The acquisition unit 131 acquires user information indicating information about a user and behavioral information indicating the user's behavior. Here, the user information is information about a user, such as information provided to an information service provider when the user uses a predetermined information service. The behavioral information is information indicating the user's behavior using the predetermined information service, which is generated as a result of the user's use of the predetermined information service. After acquiring the user information, the acquisition unit 131 stores the acquired user information in the user information storage unit 121. After acquiring the behavioral information, the acquisition unit 131 stores the acquired behavioral information in the behavioral information storage unit 122. Note that the acquisition unit 131 may acquire either or both of the user information and the behavioral information at each predetermined date and time, or may acquire either or both of the user information and the behavioral information every time the user accesses the information processing device 100.

[0064] Furthermore, the source from which the acquisition unit 131 acquires user information and behavioral information is not limited to the user terminal 200, but may be acquired from other server devices that provide various information services such as search services, shopping services, payment services, route search services, map provision services, travel services, restaurant introduction services, weather forecast services, schedule management services, news provision services, auction services, video content distribution services, and financial trading (stock trading, etc.) services, or may be acquired from external storage media.

[0065] (Regarding the calculation unit 132) The calculation unit 132 uses a plurality of models to calculate an index for a specific feature of the user for each model. When user information and behavioral information are input, the calculation unit 132 inputs the user information and behavioral information into a model that calculates an index for the specific feature, thereby calculating an index for the specific feature of the user for each model. Note that the models used to calculate the index are read from the model storage unit 123 and used, and the index for the specific feature of the user is calculated for each model. Furthermore, the number of models used by the calculation unit 132 to calculate an index for the specific feature of the user may be any number.

[0066] (Regarding Question 133) The questioning unit 133 asks a question related to the specific feature to a user for whom an index related to the specific feature has been calculated. As shown in FIG. 1 , the calculation unit 132 calculates an index related to the specific feature for each user. Therefore, the questioning unit 133 asks a question related to the specific feature to a user for whom the calculation unit 132 has calculated an index related to the specific feature. For example, the questioning unit 133 may ask a question related to the specific feature to the user terminals 200 of multiple users for whom the calculation unit 132 has calculated an index related to the specific feature by transmitting question information, in a multiple-choice or input format, asking whether or not the specific feature "likes glasses." In this case, the questioning unit 133 may cause the multiple user terminals 200 that have received the question information to display the question information on the output unit 230, and may receive answer information from the user terminal 200 that a user U inputs via the input unit 220 of the user terminal 200 after confirming the question related to the specific feature displayed on the user terminal 200.

[0067] Furthermore, the questioning unit 133 asks the user a multiple-choice question about whether or not the user has a specific characteristic, as a question related to the specific characteristic. For example, the questioning unit 133 may transmit question information including the question, "Do you like glasses? Please answer 'yes' or 'no'," to the user terminals 200 of multiple users for whom an index related to the specific characteristic has been calculated, and the multiple user terminals 200 that receive the question information may display the question text and selection boxes for 'yes' and 'no' on the output unit 230, thereby asking the user a multiple-choice question about whether or not the user has the specific characteristic. Furthermore, the questioning unit 133 accepts answer information input by the users from the multiple user terminals 200 that have received the question information.

[0068] Furthermore, the questioning unit 133 may ask the user a question for each classification of the specific feature. The classification of the specific feature refers to the name of the classification when the specific feature is classified into multiple categories, and may be, for example, "gourmet," "sports," "movies," or "fashion." For example, in the case of a specific feature belonging to the "gourmet" category, the questioning unit 133 may ask the user a question for each classification of the specific feature by transmitting question information including the question, "Do you like bananas? Please answer 'yes' or 'no'." The questioning unit 133 asks the user a question for each classification of the specific feature and accepts answer information input by the user from multiple user terminals 200.

[0069] (Regarding the confirmation unit 134) The confirmation unit 134 checks whether there is a discrepancy between the calculation result of the index for the specific feature of the user and the answer related to the specific feature of the user. For example, suppose that the calculation unit 132 calculates the index for a specific feature of user U1, such as "liking glasses," to be 0.8. In this case, suppose that the questioning unit 133 sends a question related to the specific feature, "Do you like glasses?" to the user terminal 200A of user U1, and user U1 answers "yes." In this case, the confirmation unit 134 outputs a confirmation result indicating that there is no discrepancy between the calculation result of the index for the specific feature of user U1 and the answer related to the specific feature of user U1. Note that the confirmation unit 134 checks whether there is a discrepancy between the calculation result of the index for the specific feature of the user and the answer to the question related to the specific feature of the user for all users for whom the calculation unit 132 calculated the index for the specific feature, the questioning unit 133 asked the question related to the specific feature, and an answer was obtained.

[0070] The confirmation unit 134 may use another model similar to the predetermined model to determine discrepancies with the respondent. For example, for models that are expected to have similar answer distributions, such as a "likes bananas" model and a "likes banana cake" model, it may be determined that there is a discrepancy in the answer of a respondent who gives a different answer to a question corresponding to these models. The confirmation unit 134 may cross multiple models in this way, that is, compare answers from similar models, to discover discrepancies.

[0071] (Regarding the evaluation unit 135) The evaluation unit 135 evaluates the appropriateness of the user's answer based on the user's answer to the question posed by the questioning unit 133 and the index for the specific feature calculated for the user. For example, if the index for the user's specific feature is calculated to be 0.8 and the user answers "yes," the evaluation unit 135 evaluates the appropriateness of the user's answer as high. Specifically, the evaluation unit 135 may evaluate the appropriateness of the user's answer by expressing it as a number in the range of 0 to 1, and may evaluate it as 0.8, for example.

[0072] Furthermore, the evaluation unit 135 evaluates the user for each category of specific features and outputs the user's evaluation for each category of specific features. That is, when the questioning unit 133 asks the user a question for each category of specific features, or when the questioning unit 133 has already asked questions about multiple specific features and answers to the questions related to the multiple specific features have been obtained, the evaluation unit 135 evaluates the user for each category of specific features and outputs the user's evaluation for each category of specific features. Specifically, for example, the user may be evaluated for each category of specific features based on whether or not there is a discrepancy between the calculated results of indices for the specific features related to "food" and "sports" and the user's answers. That is, the evaluation unit 135 comprehensively evaluates whether or not there is a discrepancy between the indices for the user's multiple specific features related to "food" and the user's answers regarding them. For example, the evaluation unit 135 may calculate an evaluation value for each specific feature based on whether or not there is a discrepancy between the indicators of multiple specific features related to "gourmet" and the user's answers, and may evaluate the user for each category of specific features by calculating the average value for multiple specific features that belong to the "gourmet" category.

[0073] (Regarding Proposal Section 136) The suggestion unit 136 proposes a method for improving the user's rating evaluated by the evaluation unit 135 from the perspective of the way the question unit 133 asks the question and the classification of the specific feature. For example, suppose that when the question unit 133 asks multiple-choice questions, user U1 tends to give higher ratings compared to when the question unit 133 asks input-based questions. Furthermore, suppose that the ratings of answers to questions about "gourmet" as a classification of specific feature of user U1 tend to be significantly higher compared to other classifications of specific feature. In this case, the suggestion unit 136 proposes asking a "multiple-choice" question for user U1 and a "multiple-choice" question about "gourmet" as a classification of specific feature. That is, the suggestion unit 136 analyzes the user's rating trends from the perspective of the way the user asks the question and the classification of the specific feature, and proposes a way of asking a question and a classification of the specific feature that are likely to improve the user's rating.

[0074] [4. User terminal configuration] Next, the configuration of the user terminal 200 according to the embodiment will be described with reference to Fig. 7. Fig. 7 is a diagram showing an example of the configuration of the user terminal according to the embodiment. As shown in Fig. 7, the user terminal 200 has a communication unit 210, an input unit 220, an output unit 230, and a control unit 240.

[0075] The communication unit 210 is realized by, for example, a NIC etc. The communication unit 210 is connected to a network N by wire or wirelessly, and transmits and receives various information to and from the information processing device 100 via the network N.

[0076] Various types of operation information are input from the user to the input unit 220. For example, the input unit 220 may accept various operations from the user via a display surface (e.g., the output unit 230) using a touch panel. The input unit 220 may also accept various operations from buttons provided on the user terminal 200 or a keyboard or mouse connected to the user terminal 200.

[0077] The output unit 230 is a display screen of a tablet terminal or the like realized by, for example, a liquid crystal display or an organic EL (Electro-Luminescence) display, and is a display device for displaying various information. In other words, if the input unit 220 of the user terminal 200 is a touch panel, the display screen of the output unit 230 accepts input from the user and also outputs the input to the user. The output unit 230 may also be a speaker, and may output sound from the speaker.

[0078] The control unit 240 is realized, for example, by a CPU, an MPU, or the like executing various programs stored in the user terminal 200 using RAM as a work area. The control unit 240 may also be realized, for example, by an integrated circuit such as an ASIC or an FPGA.

[0079] As shown in FIG. 7, the control unit 240 includes a receiving unit 241.

[0080] The reception unit 241 receives answers to questions from users. The reception unit 241 receives question information transmitted from the information processing device 100 via the communication unit 210, displays the question information on the output unit 230, and receives answers to the questions from users via the input unit 220. Note that the answer information received by the reception unit 241 may include "yes" or "no" that the user has answered to the question included in the question information, or a sentence that expresses the answer to the question.

[0081] [5. Information Processing Flow] Next, a procedure of information processing by the information processing device 100 according to the embodiment will be described with reference to FIG. 8. FIG. 8 is a flowchart showing an example of information processing according to the embodiment. For example, the information processing device 100 acquires user information indicating information about a user and behavioral information including the user's search history (step S101). Then, the information processing device 100 stores a plurality of models that have been trained to learn the relationships between the user information, the behavioral information, and specific characteristics of the user (step S102). Then, the information processing device 100 calculates an index for the specific characteristic for each user using the plurality of models (step S103). Then, the information processing device 100 asks a question related to the specific characteristic to the user for whom the index for the specific characteristic has been calculated (step S104). Then, the information processing device 100 evaluates the appropriateness of the user's answer to the question based on the user's answer (step S105).

[0082] [6. Hardware Configuration] The information processing device 100 according to the above-described embodiment is realized by a computer 1000 having a configuration as shown in Fig. 9, for example. Fig. 9 is a hardware configuration diagram showing an example of a computer that realizes the functions of the information processing device. 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 IF (Interface) 1060, an input IF 1070, and a network IF 1080 are connected via a bus 1090.

[0083] The arithmetic device 1030 operates based on programs stored in the primary storage device 1040 and secondary storage device 1050, programs read from the input device 1020, and the like, and executes various processes. The primary storage device 1040 is a memory device, such as a RAM, that temporarily stores data used by the arithmetic device 1030 for various calculations. The secondary storage device 1050 is a storage device that stores data used by the arithmetic device 1030 for various calculations and various databases, and is realized by a ROM (Read Only Memory), an HDD (Hard Disk Drive), a flash memory, or the like.

[0084] The output IF 1060 is an interface for transmitting information to be output to an output device 1010 that outputs various types of information, such as a monitor or a printer, 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 IF 1070 is an interface for receiving information from various input devices 1020, such as a mouse, keyboard, scanner, etc., and is realized by a USB, etc.

[0085] The input device 1020 may be a device that reads information from, for example, an optical recording medium such as a CD (Compact Disc), a DVD (Digital Versatile Disc), or a PD (Phase Change Rewritable Disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory. The input device 1020 may also be an external storage medium such as a USB memory.

[0086] The network IF 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.

[0087] The arithmetic unit 1030 controls the output device 1010 and the input device 1020 via the output IF 1060 and the input IF 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.

[0088] For example, when the computer 1000 functions as the information processing device 100, the arithmetic unit 1030 of the computer 1000 realizes the functions of the control unit 130 of the information processing device 100 by executing a program loaded onto the primary storage device 1040.

[0089] [7. Composition and Effects] The information processing device 100 according to the present disclosure includes an acquisition unit 131 that acquires user information indicating information about a user and behavioral information including the user's search history, a model storage unit 123 that stores a plurality of models that have been trained to learn the relationship between the user information, the behavioral information, and specific characteristics of the user, a calculation unit 132 that calculates an index for the specific characteristic for each user using the plurality of models, a questioning unit 133 that asks questions related to the specific characteristic to the user for whom the calculation unit 132 has calculated the index for the specific characteristic, and an evaluation unit 135 that evaluates the appropriateness of the user's answer based on the user's answer to the question asked by the questioning unit 133 and the index for the specific characteristic calculated for the user.

[0090] According to this configuration, a user can be evaluated based on whether or not there is a discrepancy between the calculation result of the index related to the specific feature of the user and the answer to the question related to the specific feature of the user. Therefore, it is possible to provide the information processing device 100 that can evaluate a user whose attribute information is to be estimated.

[0091] The questioning unit 133 of the information processing device 100 according to the present disclosure asks the user a multiple-choice question about whether or not the user has a specific characteristic, as a question related to the specific characteristic.

[0092] This configuration makes it possible to obtain a clear answer, rather than an ambiguous answer, as to whether or not a user has a specific characteristic, thereby providing the information processing device 100 capable of evaluating a user whose attribute information is to be estimated.

[0093] The questioning unit 133 of the information processing device 100 according to the present disclosure asks the user questions for each specific feature classification, and the evaluation unit 135 evaluates the user for each specific feature classification and outputs the user's evaluation for each specific feature classification.

[0094] This configuration makes it possible to distinguish between areas in which the user is good and areas in which the user is not good, and therefore allows the user to be evaluated appropriately without being evaluated based on answers in a specific area.

[0095] The information processing device 100 according to the present disclosure further includes a suggestion unit 136 that suggests a method for improving the evaluation of the user evaluated by the evaluation unit 135 from the viewpoint of the way the questioning unit 133 asks questions and the classification of specific features.

[0096] This configuration provides a method for improving the user's evaluation in terms of how to ask questions to the user and the classification of specific features.

[0097] The information processing method according to the present disclosure includes the steps of acquiring user information indicating information about a user and behavioral information including the user's search history, storing multiple models of the relationship between the user information, the behavioral information, and specific characteristics of the user, calculating an index for the specific characteristics for each user using the multiple models, asking questions related to the specific characteristics to the users for whom the index for the specific characteristics has been calculated, and evaluating the appropriateness of the user's answers to the questions based on the user's answers to the questions.

[0098] According to this configuration, a user can be evaluated based on whether or not there is a discrepancy between the calculation result of the index related to the specific characteristic of the user and the answer to the question related to the specific characteristic of the user, thereby providing an information processing method capable of evaluating a user whose attribute information is to be estimated.

[0099] The information processing program disclosed herein causes a computer to perform the following steps: acquiring user information indicating information about a user and behavioral information including the user's search history; storing multiple models that have been trained to learn the relationship between the user information, behavioral information, and specific characteristics of the user; calculating an index for the specific characteristics for each user using the multiple models; asking questions related to the specific characteristics to the users for whom the index for the specific characteristics has been calculated; and evaluating the appropriateness of the user's answers to the questions based on the user's answers to the questions.

[0100] According to this configuration, a user can be evaluated based on whether or not there is a discrepancy between the calculation result of the index related to the specific characteristic of the user and the answer to the question related to the specific characteristic of the user. Therefore, it is possible to provide an information processing program capable of evaluating a user whose attribute information is to be estimated.

[0101] The above describes the embodiments of the present application in detail based on the drawings, but this is merely an example, and the present invention can be implemented in other forms that include the embodiments described in the Disclosure of the Invention section and that have been modified and improved in various ways based on the knowledge of those skilled in the art.

[0102] Furthermore, the above-mentioned "section, module, unit" can be read as "means" or "circuit," etc. For example, the acquisition unit 131 can be read as acquisition means or acquisition circuit. [Explanation of symbols]

[0103] 100 Information processing device 110 Communications Department 120 Storage section 121 User information storage unit 122 Behavior information storage unit 123 Model Memory Unit 130 Control Unit 131 Acquisition Department 132 Calculation Unit 133 Questions 134 Verification Department 135 Evaluation Department 136 Proposal Department 200 User terminals 210 Communications Department 220 Input section 230 Output section 240 Control Unit 241 Reception Department N Network

Claims

1. an acquisition unit that acquires user information indicating information about a user and behavioral information including a search history of the user; a model storage unit that stores a plurality of models that have been trained to relate the user information, the behavioral information, and specific characteristics of the user; a calculation unit that calculates an index of a specific feature for each user using the plurality of models; a questioning unit that asks a question related to the specific feature to the user whose index related to the specific feature has been calculated by the calculation unit; an evaluation unit that evaluates the appropriateness of the user's answer based on the user's answer to the question posed by the questioning unit and the index of the specific feature calculated for the user; Information processing device.

2. the questioning unit asks the user a multiple-choice question about whether or not the user has the specific characteristic, as the question related to the specific characteristic; The information processing device according to claim 1 .

3. the questioning unit asks the user a question for each category of specific features; the evaluation unit evaluates the user for each specific feature classification and outputs the user's evaluation for each specific feature classification. The information processing device according to claim 2 .

4. a suggestion unit that suggests a method for improving the evaluation of the user evaluated by the evaluation unit from the viewpoint of the manner in which the questioning unit asks questions and the classification of the specific feature; The information processing device according to any one of claims 1 to 3.

5. An information processing method executed by a computer, comprising: acquiring user information indicating information about the user and behavioral information including the user's search history; Storing a plurality of models that have been trained to relate the user information, the behavioral information, and specific characteristics of the user; calculating an index for a specific feature for each user using the plurality of models; a step of asking a question related to a specific feature to a user who has calculated an index related to the specific feature; and evaluating the appropriateness of the user's answer based on the user's answer to the question. Information processing methods.

6. acquiring user information indicating information about the user and behavioral information including the user's search history; Storing a plurality of models that have been trained to relate the user information, the behavioral information, and specific characteristics of the user; calculating an index for a specific feature for each user using the plurality of models; a step of asking a question related to a specific feature to a user who has calculated an index related to the specific feature; evaluating the appropriateness of the user's answer based on the user's answer to the question; An information processing program that causes a computer to execute the above.

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