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

Abstract

This enables verification of the reliability of the generated model. [Solution] The information processing device is characterized by having a first generation unit that generates first quantified information that quantifies the relationship between the search query entered by each seed user and a specified action based on the commonality of the search queries entered by multiple seed users who performed the specified action; a second generation unit that generates second quantified information that quantifies the relationship between the specified action and each user based on the search query entered by each user and the first quantified information; and a display unit that displays each user information aligned based on the second quantified information in a manner that allows seed users and non-seed users to be distinguished.

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] There are known methods for predicting user behavior on the Internet.

[0003] For example, Patent Document 1 proposes a technology that learns the search query trends of users on the Internet, calculates a relevance score between the learned search query trends and the user's subsequent behavior to generate a model, and uses this model to predict the behavior of other users, thereby making it possible to predict user behavior with higher accuracy than before. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2016-177377 Summary of the Invention [Problem to be solved by the invention]

[0005] However, this system had the problem that it was unclear whether the reliability of the generated model was guaranteed.

[0006] The present application has been made in view of the above, and aims to provide a method for verifying the reliability of a generated model. [Means for solving the problem]

[0007] An information processing device according to an embodiment of the present disclosure is characterized by having a first generation unit that generates first quantified information that quantifies the relationship between the search query entered by each seed user and a predetermined behavior based on the commonality of the search queries entered by multiple seed users who performed the predetermined behavior; a second generation unit that generates second quantified information that quantifies the relationship between the predetermined behavior and each user based on the search query entered by each user and the first quantified information; and a display unit that displays the user information arranged based on the second quantified information in a manner that allows seed users and non-seed users to be distinguished. [Effects of the Invention]

[0008] According to one aspect of the embodiment, the reliability of the generated model can be verified. [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 information obtained by graphing the second digitized information according to the embodiment. [Figure 3] FIG. 3 is a diagram showing an example in which the proportion of non-seed users is superimposed on a graph. [Figure 4] FIG. 4 is a diagram showing an example of the configuration of the information processing device 10 according to the embodiment. [Figure 5] FIG. 5 is a diagram illustrating an example of the first digitized information according to the embodiment. [Figure 6] FIG. 6 is a diagram illustrating an example of the second digitized information according to the embodiment. [Figure 7] FIG. 7 is a flowchart showing the information processing procedure by the information processing device 10 according to the embodiment. [Figure 8] FIG. 8 is a hardware configuration diagram showing an example of a computer 1000 that realizes the functions of the information processing device 10. As shown in FIG. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, a detailed description will be given of an information processing device, an information processing method, and an information processing program (hereinafter referred to as an "embodiment") according to the present application, 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 embodiment. Furthermore, the same components in the following embodiments are denoted by the same reference numerals, and redundant description will be omitted.

[0011] (Embodiment) [1. Information Processing] 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. In the example shown below, the target is a user, and all users are referred to as each user, and a portion of users who have taken a predetermined action among all users is referred to as a seed user. Note that the target is not limited to a user, and may be any target from which information can be collected, such as a city, a product, or a service.

[0012] First, the information processing device 10 stores a part of a group of users who have taken a predetermined action as a seed user U1 (step S1). Whether or not the predetermined action has been taken may be determined by a user questionnaire or the like, or may be determined based on a communication history between the terminal device 100 owned by the seed user U1 and the information processing device 10.

[0013] For example, the name of a certain predetermined store may be input as a search query, and a user who then actually visits the certain predetermined store may be set as seed user U1. As another example, the name of an event may be input as a search query, and a participant who then actually attends the event may be set as seed user U1. As yet another example, the name of a certain product may be input as a search query, and a person who then actually purchases a product with that name may be set as seed user U1. Furthermore, the information processing device 10 may set as seed user U1 a person who actually visits a certain predetermined store (for example, a store corresponding to a distributed advertisement). Furthermore, the information processing device 10 may set as seed user U1 a person who has viewed a certain advertisement, web page, movie, music, or other content.

[0014] That is, the information processing device 10 estimates a user who has performed any expected action as a seed user U1 based on various information such as various purchase histories, usage histories, location histories collected by GPS, etc. For such estimation, any of various known techniques can be adopted.

[0015] Next, the information processing device 10 quantifies the relationship between the search queries entered by each seed user U1 and a predetermined behavior based on the commonality of the search queries entered by the seed users U1 (step S2). This quantified information is defined as first quantified information.

[0016] The first quantified information can be obtained, for example, as follows: When the search query entered by the seed user U1 is taken as a positive example and the search query entered by a randomly selected user is taken as a negative example, the learning model is trained so that it outputs a higher value when a positive example is input and a lower value when a negative example is input. Any method can be used for the training. For example, if a neural network is used as the model, this can be achieved by backpropagation or the like.

[0017] The learning model may be trained to output a higher numerical value for search queries commonly input by the seed user U1. For example, the learning model may be trained to output a higher value the more seed users U1 input search queries that are positive examples. The learning model may also be trained to output a smaller value (negative value) the more users input search queries that are negative examples. A list of pairs of each search query and the score output when the search query is input into such a learning model is generated as a "model." This "model" is an example of first quantified information in this embodiment. That is, the information processing device 10 generates, as first quantified information, a pair of the search query input by the seed user U1 who performed a predetermined action and the score indicating the relationship between the predetermined action and the search query.

[0018] A list of pairs of each search query and the score output when the search query is input into such a learning model is generated as a "model." This "model" is one example of the first quantified information in this embodiment. That is, the information processing device 10 generates, as the first quantified information, a pair of the search query input by the seed user U1 who performed a predetermined action, the score indicating the relationship between the predetermined action, and the search query.

[0019] Furthermore, the information processing device 10 stores the search queries input by all users including the seed user U1 (step S3). Then, the information processing device 10 digitizes the relationship between the predetermined behavior and each user based on the search queries input by all users and the first digitized information (step S4). This digitized information is used as second digitized information.

[0020] For example, the information processing device 10 identifies scores associated with search queries entered by users other than the seed user U1 from a model, which is the first quantified information. Then, the information processing device 10 calculates a score indicating the relationship between the behavior performed by the seed user U1 and the user as the second quantified information from the sum of the identified scores. Here, for example, a user who has a high relationship with a predetermined behavior has a high second quantified information, and a user who has a low relationship with the predetermined behavior has a low second quantified information.

[0021] The second quantified information is calculated, for example, as follows: For each of the multiple search queries entered by the user, the associated score (first quantified information) is obtained, and the sum of the scores is calculated. This sum is used as the second quantified information. In this way, the relationship between the multiple search queries entered by the user and a specific behavior can be expressed numerically, thereby achieving more reliable quantification compared to the quantification of a single search query.

[0022] After calculating the second quantified information for each user in this way, the users are ranked in descending order of the value of the second quantified information. In addition, a list of search queries entered by each user is generated in order of rank. Such a list becomes a list (score list) that indicates which users entered search queries that are highly relevant to a specific behavior. Such a list is used for analyzing the relevance between search queries and a specific behavior, analyzing users, etc. Note that such a list is created for each behavior set when selecting seed user U1.

[0023] Here, when the second digitized information is used, it is believed that it is possible to provide information that is likely to interest the user. For example, a list corresponding to an action of visiting a certain store includes a ranking of users who are highly relevant to that action. Therefore, it is believed that the distribution server that distributes advertisements can improve the effectiveness of advertisements by providing advertisements related to that store to users with high rankings.

[0024] On the other hand, when users are ranked in order of the degree of relevance to a predetermined behavior based on the second quantified information, it is unclear how high a user's rank is in reality related to the predetermined behavior. In other words, although it is assumed that a high value of the second quantified information means that a user is likely to take the predetermined behavior, it is difficult to estimate how high the second quantified information needs to be in order to have a high relevance to the predetermined behavior or whether the user is actually capable of taking the predetermined behavior. Furthermore, there has been insufficient verification as to whether a high value of the second quantified information can be said to indicate a high relevance to the predetermined behavior.

[0025] Therefore, after step S4, the information processing device 10 visualizes the distribution of seed users among the users arranged based on the second digitized information and presents it to the operator OP so that it can be seen (step S5). For example, the information processing device 10 displays the user information arranged based on the second digitized information in a manner that allows the seed users and non-seed users to be distinguished.

[0026] For example, the information processing device 10 may highlight search queries entered by seed users or search queries entered by non-seed users in the list generated as the second digitized information, or may display them in text of different colors. Furthermore, the information processing device 10 may display the second digitized information with different background colors set for each. That is, when displaying non-seed users, the information processing device 10 makes them identifiable by differentiating at least one display mode of color, shape, or pattern from the display mode of seed users.

[0027] Furthermore, for example, the information processing device 10 may visually display the proportion of seed users among users sorted by a predetermined number of users based on the second digitized information. For example, Fig. 2 is a diagram showing an example of information obtained by graphing the second digitized information according to the embodiment. For example, the information processing device 100 generates a graph in which the ranking in the second digitized information is plotted on the horizontal axis, the score indicating the relationship between users of each rank and a predetermined behavior is plotted on the vertical axis, and the score of each user is plotted.

[0028] Then, the information processing device 10 displays the seed user U1 in a display mode different from that of the non-seed users. According to the example of Fig. 2, the information processing device 10 displays the relationship between the ranking and score of each seed user separately from that of the non-seed users by indicating the position where the score of each seed user is plotted with circle icons 7000a to 7000f. Note that the information processing device 10 may also conversely display icons at the positions where the scores of the non-seed users are plotted.

[0029] When such a graph is provided, a user can verify the validity of the first quantified information and the second quantified information by observing the distribution of seed user U1. For example, since seed user U1 is a user who performed a predetermined behavior, it is expected that his / her score will inevitably increase and his / her ranking in the second quantified information will increase. Therefore, if the distribution of seed user U1 is such that seed user U1 is concentrated near the origin of the graph based on the second quantified information, the user can infer that the first quantified information and the second quantified information are able to appropriately express the relevance to the predetermined behavior. On the other hand, if the distribution of non-seed users is such that seed user U1 is concentrated near the origin of the graph based on the second quantified information, or if the distribution of seed user U1 is scattered, or if seed user U1 is distributed far from the origin, the user can infer that the first quantified information and the second quantified information are unable to appropriately express the relevance to the predetermined behavior.

[0030] The display unit may also visually display the proportion of seed users among the users sorted by a predetermined number of users based on the second digitized information. For example, FIG. 3 is a diagram showing an example in which the proportion of non-seed users is superimposed on a graph. As shown in FIG. 3, the information processing device 10 generates a graph plotting the scores of each user based on the second digitized information. Furthermore, the information processing device 10 groups the predetermined number of users in descending order of score. For example, the information processing device 10 groups the users in descending order of score, such as 1st to 99th place and 100th to 199th place, and calculates the proportion of non-seed users included in each group.

[0031] The information processing device 10 then superimposes information indicating the proportion of non-seed users included in each group on a graph. For example, the information processing device 10 displays a diagram in which a bar graph indicating the calculated proportion is superimposed on a graph showing the change in each user's score. For example, in the example shown in FIG. 3 , a bar graph 8000a representing the proportion of seed users among the top 100 people in the second quantified information, a bar graph 8000b representing the proportion of seed users among users numbered 101 to 200, a bar graph 8000c representing the proportion of seed users among users numbered 201 to 300, a bar graph 8000d representing the proportion of seed users among users numbered 301 to 400, and a bar graph 8000e representing the proportion of seed users among users numbered 401 to 500 are superimposed on the graph showing the change in each user's score.

[0032] When such visualization is performed, the user can infer that the first quantified information and the second quantified information are able to appropriately express the relevance to a predetermined behavior if the seed user U1 is included more frequently among the top users among the users included in the second quantified information. Furthermore, the user can infer that the first quantified information and the second quantified information are able to appropriately express the relevance to a predetermined behavior if the seed user U1 among the users included in the second quantified information is distributed unevenly or is distributed in a low score range.

[0033] The information processing device 10 may also notify the operator OP based on the evaluation result of the evaluation unit regarding the distribution trend of seed users among the users sorted based on the second digitized information. For example, if the distribution of seed user U1 is not concentrated at the top, it is known that the seed user has taken a predetermined action, and therefore there is a low correlation between the predetermined action and the ranking of the second digitized information. In this case, the validity of the first digitized information may also be questioned. In such a case, the information processing device 10 may notify the operator OP of a message urging the operator OP to reconsider the modeling of the first digitized information.

[0034] By visually presenting the data in this manner, the operator OP can visually grasp the distribution trend of the seed users. In this way, the operator OP can verify whether the first and second digitizations are appropriate.

[0035] Returning to Fig. 1, the explanation will be continued. After step S5, the information processing device 10 provides feedback on the validity of the first or second quantification to the operator based on the distribution of seed users (step S6). In this way, the operator OP can receive notification of whether the first or second quantification was valid or not, and can improve the model related to the first quantification.

[0036] The above description describes an example of the process in which the information processing device 10 displays a graph. However, the embodiment is not limited to this. The information processing device 10 may transmit information for displaying the generated graph to various information processing devices (i.e., external devices) used by the user and display the graph.

[0037] 2. Configuration of Information Processing Device Next, the configuration of the information processing device 10 according to the embodiment will be described with reference to FIG. 4. FIG. 4 is a diagram illustrating an example of the configuration of the information processing device 10 according to the embodiment. The information processing device 10 generates first quantified information that quantifies the relationship between the search query entered by each seed user U1 and the predetermined behavior based on the commonality of the search queries entered by the seed users U1 who performed the predetermined behavior. Furthermore, the information processing device 10 generates second quantified information that quantifies the relationship between the predetermined behavior and each user based on the generated first quantified information and the search query entered by each user. Furthermore, each user is classified into two or more clusters based on the commonality of the search queries entered by each user. As shown in FIG. 4, the information processing device 10 includes a communication unit 20, a storage unit 30, and a control unit 40. The information processing device 10 may also include a display unit that displays various information and an input unit that inputs various information.

[0038] The communication unit 110 is realized by, for example, a NIC etc. The communication unit 110 is connected to a predetermined network via wired or wireless connection, and transmits and receives information to and from external terminal devices and information processing devices.

[0039] (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 a hard disk or an optical disk. The storage unit 120 according to the embodiment may store information about each user, first digitized information, and second digitized information.

[0040] (First digitized information) FIG. 5 is a diagram illustrating an example of first quantified information according to an embodiment. As shown in FIG. 3, the first quantified information quantifies the relationship between a search query entered by a seed user U1 and a predetermined behavior performed by the seed user U1. In the example shown in FIG. 5, for example, if there is an all-you-can-eat steak chain called "Steak Shochikubai," and the behavior of actually visiting a Steak Shochikubai restaurant is assumed to be the predetermined behavior of the seed user U1, it can be seen that there is a very high correlation between the behavior of entering "Steak Shochikubai" as a search word and the behavior of actually visiting "Steak Shochikubai." It can also be seen that relatively high scores are assigned to search words that represent attributes of "Steak Shochikubai," such as "all-you-can-eat," and search words for meat dishes similar to steak, such as "shabu-shabu." While the example in FIG. 5 deals with a visit to the "Steak Shochikubai" restaurant, the predetermined behavior is not limited to this. That is, the predetermined behavior may be a visit to a specific restaurant or the behavior of purchasing a specific product. In this case, individual first quantified information is generated for each behavior.

[0041] (Second digitized information) 6 is a diagram showing an example of second digitized information according to an embodiment. The second digitized information is a digitized representation of the relationship between a predetermined behavior and each user U based on the search query entered by each user U and the first digitized information. In the example shown in FIG. 6, the users are sorted in ascending order starting from the user with the highest second digitized value (hereinafter referred to as "score"). The score and search query of each user are also shown.

[0042] In Figure 6, the score is calculated by applying the first digitized information, which is information that quantifies the relevance between a predetermined behavior and the search word, to each search word in each user's search log and adding up the results. That is, for each search word in each user U's search log, a search is performed for words similar to that word in the first digitized information, and if the word is found as a result of the search, the score assigned to that word is added to the total score. This calculation is performed for each word in the search log. In this way, it is possible to numerically express the likelihood of a user taking a predetermined behavior from that user's search log, and it is expected that it will be possible to predict with high accuracy whether or not the user will take a predetermined behavior.

[0043] In the example of FIG. 3, a visit to a restaurant called "Steak Shochikubai" is handled, but as with the first digitized information, the predetermined behavior to be handled is not limited to this. That is, a visit to a specific restaurant may be the predetermined behavior, or the act of purchasing a specific product may be the predetermined behavior. In such cases, separate second digitized information is generated for each behavior.

[0044] (control unit 40) 4, the control unit 40 is realized by, for example, a CPU, an MPU, or the like executing various programs (corresponding to an example of a prediction program) stored in a storage device inside the information processing device 10 using a RAM as a work area. The control unit 40 is also realized by, for example, an integrated circuit such as an ASIC or an FPGA.

[0045] As shown in FIG. 4, the control unit 40 has a first generation unit 41, a second generation unit 42, a display control unit 43, and an evaluation unit 44, and realizes or executes the functions and actions of the information processing described below. The internal configuration of the control unit 40 is not limited to the configuration shown in FIG. 4 and may be other configurations as long as they perform the information processing described below. The connection relationship between the processing units of the control unit 40 is not limited to the connection relationship shown in FIG. 4 and may be other connection relationships. The control unit 40 may also have a receiving unit, for example, when receiving various information such as first digitized information from an external information processing device. The control unit 40 may also have a transmitting unit, for example, when transmitting second digitized information to an external information processing device.

[0046] (1st generation part) The first generation unit 41 generates first quantified information through the above-described process. In the embodiment, the first generation unit 41 generates first quantified information that quantifies the relationship between the search query and the predetermined behavior based on the commonality of the search queries entered by multiple seed users who performed the predetermined behavior. Here, the first generation unit 41 may generate first quantified information for each of the multiple predetermined behaviors.

[0047] (2nd generation part) The second generation unit 42 generates second quantified information indicating a relationship between a predetermined behavior of each user U and the search query input by each user based on the first quantified information. Here, the second generation unit 42 may generate second quantified information for each of a plurality of predetermined behaviors. Furthermore, the second generation unit may generate second quantified information including information on the search query input by each user. That is, the second quantified information may be information relating to a user ID and a score, but as shown in FIG. 4, the second quantified information may also be information stored in a search log for each user.

[0048] The second generating unit 42 may further generate a list of second digitized information only for users extracted based on information of users in the same cluster as the user whose value of the second digitized information is equal to or greater than the threshold.

[0049] (Display control unit 43) The display control unit 43 causes the display unit 50 to display each piece of user information sorted based on the second digitized information in a manner that allows seed users and non-seed users to be distinguished from each other. For example, when displaying a list of each piece of user information as a string of characters, the display control unit 43 displays the seed users and non-seed users in a manner that allows them to be distinguished from each other. The display control unit 43 may also visually display the proportion of seed users among the users sorted by a predetermined number of users based on the second digitized information. The display control unit 43 may also display non-seed users in a manner that allows them to be distinguished by changing at least one display mode of color, shape, or pattern from that of seed users. The display control unit 43 may also display non-seed users in a manner that allows them to be distinguished by changing at least one display mode of color, shape, or pattern based on the proportion of seed users among the users.

[0050] For example, the display control unit 43 may refer to the second quantified information to generate a graph plotting the scores of each user, and identify seed users and non-seed users from the corresponding first quantified information. The display control unit 43 may then generate a graph in which predetermined icons are placed at the positions where the scores of seed users and non-seed users are plotted. The display control unit 43 may also group a predetermined number of users in descending order of their scores in the second quantified information, and calculate the proportion of non-seed users or seed users belonging to each group. The display control unit 43 may then generate a graph on which a bar graph showing the calculated proportions is superimposed. The display control unit 43 may also generate second quantified information, i.e., a score list, in which the display mode of characters is changed between seed users and non-seed users.

[0051] (Evaluation Section 44) The evaluation unit 44 evaluates the second quantified information based on the classification result of the display control unit 43. For example, when the display control unit 43 performs classification, the evaluation unit 44 classifies the search results into clusters on the condition that a group of words related to a predetermined behavior is included in the search query, and if a cluster including the group of words actually has many users with high scores in the second quantified information, the evaluation unit 44 evaluates that the second quantified information appropriately assigns scores to users.

[0052] That is, the evaluation unit 44 evaluates whether the first digitized information and the second digitized information are appropriate depending on whether the seed users are distributed in the top ranks in the second digitized information. Note that the evaluation unit 44 may, for example, determine a range in which seed users are distributed in clusters among the rankings of the second digitized information as an appropriate range, and evaluate other ranges as ranges with low credibility.

[0053] (Display section 50) The display unit 50 can visually present various graphs generated by the display control unit 43 and evaluation results by the evaluation unit 44 to the user of the information processing device 10 under the control of the control unit 40. This may be realized by a liquid crystal display connected to the information processing device 10 or the like.

[0054] [3. Prediction process flow] Next, the procedure of information processing by the information processing device 10 according to the embodiment will be described with reference to Fig. 7. Fig. 7 is a flowchart showing the procedure of information processing by the information processing device 10 according to the embodiment.

[0055] 7, the information processing device 10 identifies a group of users who have taken a predetermined action as a seed user group (step S101). Then, the first generation unit 41 generates digitized information, which is the first digitized information, representing the relationship between the search query input by each seed user and the predetermined action, based on the commonality of the search queries input by the seed user group (step S102). Note that when the first digitized information is acquired, the information processing device 10 does not need to perform the processes of steps S101 and S102.

[0056] Then, the second generation unit 42 of the information processing device 10 generates quantified information based on the search query entered by each user and the first quantified information, which represents the relevance between the specified behavior and each user as second quantified information (step S103).

[0057] Next, the display unit 50 of the information processing device 10 visualizes the distribution of seed users among the users arranged based on the second digitized information (step S104). Then, the evaluation unit 44 of the information processing device 10 evaluates the distribution of the seed users and provides feedback on the validity of the first digitization or the second digitization to the operator OP (step S105).

[0058] [4. Modifications] The information processing device 10 according to the embodiment described above may be implemented in various different forms other than the above embodiment, so other embodiments of the information processing device 10 will be described below.

[0059] [4-1. Prescribed Actions] In the above-described embodiment, the information processing device 10 may generate first quantified information and second quantified information for each of a plurality of predetermined actions. In the above-described embodiment, entering the "Steak Shochikubai" restaurant was defined as the predetermined action, but the information processing device 10 may also generate first quantified information and second quantified information for each of other actions, such as the user U purchasing a predetermined product or installing a predetermined application on a terminal device owned by the user U. Furthermore, the generated second quantified information may be evaluated for each action. Furthermore, the predetermined action may be inputting a predetermined reference search query.

[0060] [4-2. Graph Generation] In the above-described embodiment, the evaluation of the second quantified information was performed based on users divided into clusters, but the evaluation method is not limited to this. For example, as shown in FIG. 3, users may be sorted in descending order of their scores based on the second quantified information and plotted on a graph with the score on the vertical axis and the user on the horizontal axis. In addition, by using a special display mode, the seed users can visually recognize whether or not they are concentrated in areas with high scores. If the seed users are concentrated in areas with high scores, it has been separately confirmed that the seed users are engaging in a predetermined behavior. Therefore, it can be said that there is a high correlation between the value of the second quantified information and the predetermined behavior, and it can be evaluated that the second quantified information has been generated appropriately.

[0061] [4-3. Notification to model creators] Furthermore, the distribution trend of seed users among the users sorted based on the second digitized information may be evaluated, and the model generator of the second digitized information may be notified based on the evaluation results. That is, as described in the previous section on graph generation, if the distribution of seed users among the users sorted based on the second digitized information is high-scored, it can be evaluated that the second digitized information has been generated appropriately. Conversely, if the distribution of seed users is sparse or concentrated among low-scored users, it can be inferred that the second digitized information has not been generated appropriately. In such cases, it may be determined that the second digitized information has not been generated appropriately, and the model generator of the second digitized information may be notified accordingly.

[0062] [4-4. Matching of names in the second digitized information and search logs] In the above-described embodiment, the evaluation of the second quantified information was performed based on users divided into clusters, but the evaluation method is not limited to this. For example, if an operator has previously assigned a name to the second quantified information, the matching rate between the category related to that name and the category of each search word in the search log can be calculated, and if the matching rate is high, it can be evaluated that appropriate second quantified information has been obtained. In this case, a possible method for determining the category from the name or search word is to create a dictionary in which various words are previously assigned categories and search from that dictionary. Alternatively, the category may be determined based on a learning model that has previously learned the degree of association between words.

[0063] Alternatively, instead of calculating the matching rate between categories, the similarity between the name and each search word may be calculated. That is, Word2Vec or the like may be used to obtain a distributed representation of words using a learning model that has previously learned the degree of association between words, and this learning model may be used to calculate the similarity between the name in the second digitized information and each search word in the search log.

[0064] [4-5. Notification based on changes in user distribution] Here, the information processing device 10 may evaluate whether the first quantified information and the second quantified information are appropriate not only based on whether the seed user is highly distributed in the ranking based on the second quantified information but also based on various distribution patterns of the seed users. For example, the information providing device 10 may provide various notifications when the seed user is distributed in the middle of the ranking or within a predetermined range. Furthermore, the information providing device 10 may provide notifications, for example, in response to changes in the distribution of the seed users. For example, the information providing device 10 may provide notifications not only when the seed user is not highly distributed in the ranking, but also when, for example, there is a change in the ranking of the seed users.

[0065] For example, the information processing device 10 generates a ranking of 150 people based on the second quantified information. In such a ranking, all of the top 50 users are seed users, 20 seed users are included between 50th and 100th place, and 40 seed users are included between 100th and 150th place. In such a case, the information processing device 10 may determine that the first quantified information and the second quantified information are appropriate because all of the top 50 users are seed users. However, in such a case, there are more seed users between 100th and 150th place than between 50th and 100th place. In this way, when seed users are ranked lower, it may be considered that the first quantified information and the second quantified information are inappropriate.

[0066] Therefore, in this way, when there are more seed users in a lower range than seed users in a certain range in the distribution of seed users in the ranking, the information processing device 10 may determine that the first digitized information and the second digitized information are inappropriate. In other words, the information providing device 10 may determine whether the first digitized information and the second digitized information are appropriate depending on the tendency and mode of distribution of seed users in the ranking based on the second digitized information.

[0067] [5. Seeding Activities] In the above example, the information processing device 10 determines a predetermined action performed by a user designated as a seed user as a seed action. However, the embodiment is not limited to this. For example, the information providing device 10 may determine a user who has made a search query or posted on an SNS about "hamburger chain store A" as a "user who likes hamburger chain store A," and generate first quantified information and second quantified information about such a "user who likes hamburger chain store A." Furthermore, the information processing device 10 may generate first quantified information and second quantified information according to the attributes of the user, such as "a user in his 30s who likes hamburger chain store A."

[0068] Furthermore, the information processing device 10 may generate the first quantified information and the second quantified information by regarding users who have performed a plurality of predetermined actions having a predetermined relationship as seed users. For example, the information processing device 10 may regard not only users who purchase camping equipment but also users who purchase ski equipment as seed users who "like the outdoors" or "likely have children," and generate the first quantified information and the second quantified information.

[0069] That is, the information processing device 10 may regard any user as a seed user as long as it regards a user who can be classified into a predetermined category as a seed user and generates the first quantified information and the second quantified information. Then, if the information processing device 10 determines the seed user based not only on a single behavior but also on the behavior of a user who can be estimated to be classified into a predetermined category (for example, various purchase histories, search histories, registration of various attributes, or various behaviors used to estimate various attributes), it may generate the first quantified information and the second quantified information by using a user who has performed any number of any types of behaviors (for example, behavior that can be estimated to be that of a user in their 30s, behavior that registers a user in their 30s, or single or multiple predetermined behaviors that can be categorized as "outdoor enthusiasts") as a seed user.

[0070] [6. Hardware Configuration] The information processing device 10 according to the embodiment described above is realized by, for example, a computer 1000 configured as shown in Fig. 8. Fig. 8 is a hardware configuration diagram showing an example of the computer 1000 that realizes the functions of the information processing device 10. The computer 1000 has a CPU 1100, a RAM 1200, a ROM 1300, an HDD 1400, a communication interface (I / F) 1500, an input / output interface (I / F) 1600, and a media interface (I / F) 1700.

[0071] The CPU 1100 operates and controls each unit based on programs stored in the ROM 1300 or the HDD 1400. The ROM 1300 stores a boot program executed by the CPU 1100 when the computer 1000 starts up, programs that depend on the hardware of the computer 1000, and the like.

[0072] The HDD 1400 stores programs executed by the CPU 1100, data used by these programs, etc. The communication interface 1500 receives data from other devices via a predetermined network and sends it to the CPU 1100, and transmits data generated by the CPU 1100 to other devices via the predetermined network.

[0073] The CPU 1100 controls output devices such as a display and a printer, and input devices such as a keyboard and a mouse, via the input / output interface 1600. The CPU 1100 acquires data from the input devices via the input / output interface 1600. The CPU 1100 also outputs generated data to the output devices via the input / output interface 1600.

[0074] Media interface 1700 reads a program or data stored in recording medium 1800 and provides it to CPU 1100 via RAM 1200. CPU 1100 loads the program or data from recording medium 1800 onto RAM 1200 via media interface 1700 and executes the loaded program. Recording medium 1800 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or a PD (Phase Change Rewritable Disc), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory.

[0075] For example, when the computer 1000 functions as the information processing device 10 according to the embodiment, the CPU 1100 of the computer 1000 executes programs loaded onto the RAM 1200 to realize the functions of the control unit 130. The CPU 1100 of the computer 1000 reads and executes these programs from the recording medium 1800, but as another example, the CPU 1100 may obtain these programs from another device via a predetermined network.

[0076] Although some of the embodiments of the present application have been described in detail above with reference to the drawings, these are merely examples, and the present invention can be implemented in other forms that incorporate various modifications and improvements based on the knowledge of those skilled in the art, including the aspects described in the Disclosure of the Invention.

[0077] [7. Other] Furthermore, among the processes described in the above embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically using known methods. Furthermore, 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.

[0078] 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.

[0079] Furthermore, the above-described embodiments can be combined as appropriate within the scope of not causing any contradiction in the processing content.

[0080] Furthermore, the above-mentioned "section, module, unit" can be read as "means" or "circuit," etc. For example, the first generation unit can be read as first generation unit means or first generation unit circuit. [Explanation of symbols]

[0081] 10. Information processing equipment 20 Communications Department 30 Storage section 40 Control Unit 41 1st generation part 42 Second generation part 43 Display control unit 44 Evaluation Department 100 Terminal Device

Claims

1. a generation unit that generates information indicating a relationship between a predetermined behavior and each user based on a relationship between a search query input by a plurality of seed users and a predetermined behavior performed by the seed users, and the search query input by each user; a display unit that arranges each piece of user information based on the information and displays each piece of user information in a manner that allows the seed user and non-seed users to be distinguished; An information processing device comprising:

2. When displaying the list of user information, the display unit displays the list in a manner that allows the seed users and non-seed users to be distinguished from each other.

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

3. The display unit visually displays the percentage of seed users among the users arranged for each predetermined number of users based on the information.

3. The information processing device according to claim 2, wherein:

4. The display unit, when displaying the non-seed users, makes the non-seed users identifiable by making at least one of the display modes of color, shape, and pattern different from the display mode of the seed users.

4. The information processing device according to claim 2 or 3.

5. The display unit changes at least one of the display modes of color, shape, and pattern based on the ratio of the seed users among the users, thereby making the users identifiable.

5. The information processing device according to claim 2, wherein the information processing device is a computer.

6. The information processing device further comprises: an evaluation unit that evaluates the tendency of distribution of seed users among the users arranged based on the information; The display unit further performs a notification based on the evaluation result of the evaluation unit.

6. The information processing device according to claim 2, wherein the information processing device is a computer.

7. An information processing method executed by an information processing device, a generating step of generating information indicating a relationship between a predetermined behavior and each user based on a relationship between a search query input by a plurality of seed users and a predetermined behavior performed by the seed users and the search query input by each user; a display step of arranging each piece of user information based on the information and displaying each piece of user information in a manner that allows the seed users and non-seed users to be distinguished; An information processing method comprising:

8. a generation step of generating information indicating a relationship between a predetermined behavior and each user based on a relationship between search queries input by a plurality of seed users and a predetermined behavior performed by the seed users, and the search queries input by each user; a display step of arranging each piece of user information based on the information and displaying each piece of user information in a manner that allows the seed user and non-seed users to be distinguished; An information processing program that causes a computer to execute the above.

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