JP6774763B2 - Estimator, estimation method, and estimation program - Google Patents

Description

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

Conventionally, a technique for providing information to a user based on a so-called context such as a user's state or situation has been provided. For example, a technique for automatically extracting context conditions and recommending information is provided.

Japanese Unexamined Patent Publication No. 2013-257668

However, with the above-mentioned prior art, it may be difficult to appropriately estimate information about the user such as the interest of the user. For example, when the context condition is automatically extracted, the context changes according to the amount of information of each user. Therefore, when the amount of information of the user is small, it is difficult to appropriately estimate the information about the user such as the interest of the user. ..

The present application has been made in view of the above, and an object of the present application is to provide an estimation device, an estimation method, and an estimation program for appropriately estimating information about a user.

The estimation device according to the present application is behavior information related to the behavior of each user, and the division information which is the behavior information divided based on the context is combined based on the tendency of the behavior information included in each of the division information. Information that is generated for each union information that includes combinations that straddle the users, and that includes an acquisition unit that acquires information about a model that predicts a predetermined target and information about a model that is acquired by the acquisition unit. Based on this, it is characterized by including an estimation unit that estimates information about the user.

According to one aspect of the embodiment, there is an effect that information about the user can be appropriately estimated.

FIG. 1 is a diagram showing an example of estimation processing according to the embodiment. FIG. 2 is a diagram showing an example of the generation process according to the embodiment. FIG. 3 is a diagram showing an example of the distribution process according to the embodiment. FIG. 4 is a diagram showing an example of advertisement distribution processing based on the estimated user interest according to the embodiment. FIG. 5 is a diagram showing a configuration example of the distribution device according to the embodiment. FIG. 6 is a diagram showing an example of a user information storage unit according to the embodiment. FIG. 7 is a diagram showing an example of the behavior information storage unit according to the embodiment. FIG. 8 is a diagram showing an example of the divided information storage unit according to the embodiment. FIG. 9 is a diagram showing an example of the cluster information storage unit according to the embodiment. FIG. 10 is a diagram showing an example of the learning information storage unit according to the embodiment. FIG. 11 is a diagram showing an example of the content information storage unit according to the embodiment. FIG. 12 is a diagram showing an example of the interest information storage unit according to the embodiment. FIG. 13 is a flowchart showing an example of the generation process according to the embodiment. FIG. 14 is a flowchart showing an example of the generation process according to the embodiment. FIG. 15 is a flowchart showing an example of the distribution process according to the embodiment. FIG. 16 is a flowchart showing an example of the estimation process according to the embodiment. FIG. 17 is a flowchart showing an example of advertisement distribution processing based on the estimated user interest according to the embodiment. FIG. 18 is a diagram showing an example of model allocation based on similar users according to the embodiment. FIG. 19 is a diagram showing an example of model allocation based on similar users according to the embodiment. FIG. 20 is a hardware configuration diagram showing an example of a computer that realizes the function of the distribution device.

Hereinafter, the estimation device, the estimation method, and the embodiment for implementing the estimation program (hereinafter referred to as “the embodiment”) according to the present application will be described in detail with reference to the drawings. It should be noted that this embodiment does not limit the estimation device, estimation method, and estimation program according to the present application. Further, in each of the following embodiments, the same parts are designated by the same reference numerals, and duplicate description is omitted.

(Embodiment)
[1. Estimate processing]
First, an example of the estimation process according to the embodiment will be described with reference to FIG. FIG. 1 is a diagram showing an example of estimation processing according to the embodiment. Specifically, FIG. 1 shows a case where an estimation process for estimating the interest of the user U3 is performed. In the example shown in FIG. 1, the model information MD11 including the cluster information storage unit 124 and the learning information storage unit 125 is generated by the generation process in the distribution system 1 shown in FIG. The cluster information storage unit 124 shows information about clustering when a model for predicting interest in content in each user's context is generated. Specifically, the cluster information storage unit 124 in FIG. 1 has models M1 to M6 and the like in a mass (hereinafter, may be referred to as “mass MS”) which is an area where one user and one context intersect. Of these, which model was assigned is shown. That is, the model information MD 11 includes information related to clustering of divided information included in each mass MS as shown in the cluster information storage unit 124. Further, the model information MD 11 includes information about models M1 to M6 generated for each cluster as shown in the learning information storage unit 125. The context referred to here corresponds to the state and situation of the user including the environment in which the user is placed, and the details will be described later.

The cluster information storage unit 124 in FIG. 1 indicates that each mass MS includes six clusters indicated by M1 to M6 shown in the figure. For example, each model M1 to M6 is generated based on the division information corresponding to M1 to M6 shown in each mass MS stored in the division information storage unit 123 (see FIG. 8). Although details will be described later, the divided information here means information in which the user's behavior information is divided for each context. For example, the behavior information is information that distributes content to each user and indicates whether or not each user has an action with respect to the distributed content. Further, in the mass MS in the cluster information storage unit 124, a model generated by a predetermined method is assigned to the mass MS whose symbols M1 to M6 are not shown, but the details will be described later.

For example, in FIG. 1, a mass MS corresponding to the context CT11 of the user U1, a mass MS corresponding to the context CT15 of the user U2, a mass MS corresponding to the context CT17 of the user U2, and a mass MS corresponding to the context CT13 of the user U4. Indicates that the same model M1 is assigned to. That is, it is shown that the model M1 is generated by using the union information that combines the division information included in the above four mass MSs. Further, as shown in the learning information storage unit 125, the model information regarding the model M1 is such that the weight of the feature 1 is "0.5", the weight of the feature 2 is "-0.4", and the weight of the feature 3 is "0. 2 ”etc. For example, when the distribution system 1 requests the distribution of the content in the context CT11 corresponding to the case where the user U1 is at home (home) in the morning, the distribution system 1 determines the content to be distributed to the user U1 using the model M1. The features may be set by the administrator of the distribution device 100 (see FIG. 5), or may be extracted in the model generation process. For example, the feature may be a character string (keyword) such as "economy", "smartphone", or "soccer". In addition, the features of each model may be the same or different.

Further, as shown in FIG. 1, each feature such as feature 1 to feature 3 is associated with an object (classification) to which it is related. For example, feature 1 is a keyword related to sports (for example, "match"), feature 2 is a keyword related to economy (for example, "exchange"), and feature 3 is political and international. It may be a related keyword (for example, "going out"). For example, each feature is associated with one of the objects, depending on the relationship between each and the object for which interest is to be estimated.

The distribution device 100 uses the model information MD11 to calculate the interest score in each context for each user (step S1). In FIG. 1, the distribution device 100 calculates an interest score for each target context for each user, as shown in the interest information storage unit 127. For example, the distribution device 100 calculates the interest score of the user U1 in the context CT11 for each object based on the weight of each feature of the model M1 assigned to the mass MS corresponding to the context CT11 of the user U1. For example, the distribution device 100 associates the interest score of the user U1 with the target "sport" in the context CT11 with the sports among the features of the model M1 assigned to the mass MS corresponding to the context CT11 of the user U1. Calculated by adding up the weights of features. In FIG. 1, the distribution device 100 sets the interest score of the user U1 in the context CT11 to the target “sport” by the weight “0.5” of the feature 1 of the model M1 and the weights of other features associated with the sport. Calculated by adding up. For example, the distribution device 100 calculates the interest score of the user U1 in the context CT11 for the target “sports” as “1.2”. Further, for example, the distribution device 100 calculates the interest score of the user U1 in the context CT11 for the target "economy" as "0.2", and sets the interest score of the user U1 in the context CT11 to the target "politics" to "0". .5 "is calculated. The interest score shown in the item "total" in the interest information storage unit 127 indicates the interest score obtained by adding up the interest scores of all contexts such as contexts CT11 to CT18.

In the above, the weights of the features associated with the objects are added up, but the interest score is calculated by adding up the values obtained by multiplying each weight by the coefficient according to the strength of the association of each feature with the object. May be good. In this case, the weight of the feature that is strongly related to the target has a greater influence on the interest score, so that the interest score that reflects the user's interest can be calculated more appropriately. Further, it may be a numerical value indicating the ratio of each object. For example, an interest score having a large value may be assigned to an object having a high interest of the user, and an interest score may be assigned to each object so that the total interest score of the entire object is 1. Further, the features associated with a plurality of objects may have different weights assigned to each object. For example, in FIG. 1, the features 3 associated with the two objects of politics and international may have different weights assigned to each object. For example, a value obtained by multiplying the weight of feature 3 by 0.7 may be used as the score of the target "politics", and a value obtained by multiplying the weight of feature 3 by 0.3 may be used as the score of the target "international". Further, for example, the weight of the feature 3 may be used as the score of the target "politics", and the value obtained by multiplying the weight of the feature 3 by 0.5 may be used as the score of the target "international". The allocation of the coefficient to each feature is an example, and the distribution device 100 may assign the coefficient to each feature by various methods. For example, the distribution device 100 may calculate a coefficient to be assigned to each feature based on the tendency of the content (article) selected when the model assigned to the context of each user is used. Further, for example, the distribution device 100 may assign a coefficient of feature for each model. For example, in the distribution device 100, when the ratio of the content related to the economy is large among the contents (articles) selected by the model M1, the coefficient of each element is increased so that the coefficient assigned to the element related to the economy in the model M1 becomes large. May be calculated. Further, for example, the distribution device 100 may make the coefficient corresponding to the model M1 different from the coefficient corresponding to the model M2 for each feature. For example, in the distribution device 100, when the model M2 tends to select more political content than the model M1, the coefficient corresponding to the model M1 is larger than the coefficient corresponding to the model M2 in terms of the politics-related nature. You may calculate the coefficient of each element for each model so that

The distribution device 100 estimates the user's interest using the interest score calculated in step S1 (step S2). In FIG. 1, the distribution device 100 estimates interest in user U3 as shown in interest information ES11. For example, the distribution device 100 estimates interest information ES11-1, which indicates the overall interest of the user U3 over all contexts. Here, regarding the user U3 in the interest information storage unit 127, the interest score shown in the item "total" is "-1.8" for the target "politics" and "8.1" for the target "economy". , The target "sports" is "1.4". Therefore, in FIG. 1, the distribution device 100 estimates that the user U3 as a whole is highly interested in the economy.

Further, for example, the distribution device 100 estimates interest information ES11-2 indicating interest in each context of the user U3. Here, regarding the user U3 in the interest information storage unit 127, the interest score shown in the item "CT12" is "4.5" for the target "economy", and the interest score is the highest among each target. Further, regarding the user U3 in the interest information storage unit 127, the interest score of the target "economy" is also the highest among the targets for the items "CT14", "CT16", and "CT18". Therefore, in FIG. 1, the distribution device 100 presumes that the user U3 is highly interested in the economy when the user U3 is in the contexts CT12, CT14, CT16, CT18, that is, in the office. Further, regarding the user U3 in the interest information storage unit 127, the interest score shown in the item "CT11" is "3.4" for the target "sports", and the interest score is the highest among each target. Therefore, in FIG. 1, the distribution device 100 presumes that the user U3 is highly interested in sports when the user U3 is at the context CT11, that is, at home (home) in the morning.

As described above, the distribution device 100 calculates the interest score of each target for each context for each user by using the model generated for each union information including the combination straddling the users, and the calculated target of each target. Estimate the user's interest based on the interest score. The distribution device 100 estimates the information about the user based on the information about the model generated for each union information including the combination across the users, and thereby uses the model including the behavior information of other users to relate to the user. Information can be estimated. As a result, the distribution device 100 can appropriately estimate information about the user. In the above example, the distribution device 100 calculates the interest score by adding up the weights (scores) assigned to the features, but the distribution device 100 calculates the interest score by various methods. You may. For example, the distribution device 100 may calculate a score (interest score) based on the tendency of the content (article) selected when the model assigned to each user's context is used. For example, when the distribution device 100 has a large proportion of the economy among the contents (articles) selected by the model M1 assigned to the context CT11 of the user U1, the score (interest score) regarding the economy of the context CT11 of the user U1 is large. The score (interest score) may be calculated so as to be. Further, the information about the user is not limited to the information about the target that the user is highly interested in, and may be various information about the user. For example, the information about the user may be information that estimates a user similar to the user. Details on this point will be described later. Further, the distribution using the above-mentioned interest information ES11 will be described in detail in FIG.

[2-1. Generation process]
From here, an example of the generation process according to the embodiment will be described with reference to FIG. FIG. 2 is a diagram showing an example of the generation process according to the embodiment. In the example shown in FIG. 2, the distribution device 100 generates a model for predicting the user's interest in the content as a predetermined target. Further, the distribution device 100 provides a service of distributing content to the user using the generated model, and this point will be described with reference to FIG. 2 to 4 show a case where users U1 to U4 use terminal devices 10-1 to 10-4, respectively. In the case where the terminal devices 10-1 to 10-4 are described without distinction, they are collectively referred to as the terminal device 10.

As shown in FIG. 2, the distribution system 1 includes a terminal device 10 and a distribution device 100. The terminal device 10 and the distribution device 100 are communicably connected by wire or wirelessly via a predetermined communication network (not shown). The distribution system 1 shown in FIG. 2 may include a plurality of terminal devices 10 and a plurality of distribution devices 100.

The terminal device 10 is an information processing device used by the user. The terminal device 10 is realized by, for example, a smartphone, a tablet terminal, a notebook PC (Personal Computer), a desktop PC, a mobile phone, a PDA (Personal Digital Assistant), or the like. FIG. 2 shows a case where the terminal device 10 is a smartphone.

In addition, the terminal device 10 accepts operations by the user. In the example shown in FIG. 2, the terminal device 10 requests the distribution device 100 for the content to be displayed on a predetermined application (for example, a browser or the like). In the following, the terminal device 10 may be referred to as a user. That is, in the following, the user can be read as the terminal device 10.

The distribution device 100 is an information processing device that generates a model for predicting a predetermined target for each union information in which the division information, which is the behavior information divided based on the context, is combined. Further, the distribution device 100 is an estimation device that estimates information about the user based on the information about the generated model. Further, the distribution device 100 is an information processing device that provides a distribution service that distributes content in response to a content distribution request. For example, the distribution device 100 is an information processing device that distributes content to the terminal device 10 according to a user who uses the terminal device 10 that requests the distribution of the content.

In FIG. 2, the distribution device 100 distributes content to each user, and collects behavior information indicating the presence or absence of each user's behavior with respect to the distributed content. For example, the distribution device 100 distributes content to users U1 to U4, and collects behavior information indicating whether or not each user has an action on the distributed content. For example, the distribution device 100 collects information indicating whether or not the user has an action (for example, a click of the content) with respect to the content (for example, the content CN11) distributed to the user as the action information.

First, the distribution device 100 distributes the content to the terminal device 10-1 used by the user U1 (step S11-1). Then, the distribution device 100 acquires the action information indicating the presence / absence of the action of the user U1 with respect to the distributed content from the terminal device 10-1 (step S12-1). Further, the distribution device 100 distributes the content to the terminal device 10-2 used by the user U2 (step S11-2). Then, the distribution device 100 acquires the action information indicating the presence / absence of the action of the user U2 with respect to the distributed content from the terminal device 10-2 (step S12-2). Further, the distribution device 100 distributes the content to the terminal device 10-3 used by the user U3 (step S11-3). Then, the distribution device 100 acquires the action information indicating the presence / absence of the action of the user U3 with respect to the distributed content from the terminal device 10-3 (step S12-3). Further, the distribution device 100 distributes the content to the terminal device 10-4 used by the user U4 (step S11-4). Then, the distribution device 100 acquires the action information indicating the presence / absence of the action of the user U4 with respect to the distributed content from the terminal device 10-4 (step S12-4).

It should be noted that steps S11-1 to S11-4 are for explaining the process, and any of steps S11-1 to S11-4 may be performed first, and each step S11-1 to S11-4 may be performed first. May be performed multiple times. Hereinafter, when steps S11-1 to S11-4 will be described without distinction, they are collectively referred to as step S11. Further, steps S12-1 to S12-4 are for explaining the processing, and if each is after the corresponding step S11, any of steps S12-1 to S12-4 is performed first. Each step S12-1 to S12-4 may be performed a plurality of times. Hereinafter, when steps S12-1 to S12-4 will be described without distinction, they are collectively referred to as step S12. For example, each time each step S11 is performed, step S12 may be performed to acquire action information. Further, for example, each step S11 may be performed a plurality of times, and step S12 may be performed at a predetermined timing to acquire action information.

Then, the distribution device 100 divides the information regarding the behavior of each user collected in steps S11 to S12 (step S13). Specifically, the distribution device 100 divides the collected behavior information of each user based on the context.

Here, the context means the situation of the user or the terminal device 10 or the environment (background) of the user or the terminal device 10. For example, the context is information corresponding to a situation in which the terminal device 10 is used by the user or a state in which the user who owns the terminal device 10 is placed. That is, the context in FIGS. 2 to 4 corresponds to the situation of users U1 to U4, for example, the environment where the users U1 to U4 are present. Specifically, the context in FIGS. 2 to 4 corresponds to the situation of users U1 to U4 when the content is distributed (displayed) to each user. For example, the context may be estimated based on the time when the content is delivered or the user's position information acquired from the terminal device 10. Note that each context may be exclusive or duplicated.

In addition, FIGS. 2 to 4 show contexts based on time zones and positions for the sake of simplicity. Specifically, the contexts in the examples of FIGS. 2 to 4 are four: morning (4 to 10 o'clock), noon (10 to 16 o'clock), night (16 to 22 o'clock), and midnight (22 to 4 o'clock). Eight contexts CT11-CT18 are shown based on the time zone and two locations of home (home) or office (work location).

Here, the divided information storage unit 123 in FIG. 2 shows a state in which the action information of each user is divided for each context in step S13. The divided information storage unit 123 shows a case where the user is a row and the context is a column. The area where one user and one context intersect in the divided information storage unit 123 is defined as a mass. For example, in FIG. 2, the region where the user U4 and the context CT12 intersect is defined as the mass MS42. When the position of each cell is not distinguished, it may be referred to as mass MS. The information included in each mass MS shown in FIG. 2 is divided information in which the behavior information regarding the user's behavior for each division regarding the user is divided based on the context. Further, the line in the divided information storage unit 123 may have any particle size as long as it is a division related to the user, not limited to the user. For example, the line in the divided information storage unit 123 may be classified based on the user attribute, which is a classification related to the user. The classification based on the user attribute referred to here may be a classification based on the similarity of information on the user attribute such as age, gender, and interest.

For example, the mass MS at the position where the user U1 and the context CT11 intersect (the mass MS on the upper left) includes division information indicating the presence or absence of actions such as actions AT11, AT12, and AT13. For example, the mass MS at the position where the user U4 and the context CT18 intersect (the mass MS at the lower right) includes division information indicating the presence or absence of actions such as actions AT48 and AT11.

Further, “◯” shown on the right side of the action AT11 or the like in each mass MS in the divided information storage unit 123 indicates that the user has performed the corresponding action. Further, "x" shown on the right side of the action AT11 or the like in each mass MS in the divided information storage unit 123 indicates that the user did not perform the corresponding action. For example, in the mass MS at the position where the user U1 and the context CT11 intersect, the action AT11 is “◯”, indicating that the user U1 has performed the action AT11 at the morning house. Further, for example, in the mass MS at the position where the user U1 and the context CT11 intersect, the action AT12 is “x”, indicating that the user U1 did not perform the action AT12 in the morning house. Further, for example, in the mass MS at the position where the user U1 and the context CT11 intersect, the action AT13 is “◯”, indicating that the user U1 has performed the action AT13 in the morning house.

Then, the distribution device 100 generates a model by using the division information of each mass MS in the division information storage unit 123 (step S14). For example, the distribution device 100 clusters the division information of each mass MS in the division information storage unit 123, and generates a model for each cluster. For example, the distribution device 100 generates a model that predicts a user's interest in content.

For example, the distribution device 100 may perform clustering by a clustering method in which the number of clusters is not set. Further, for example, the distribution device 100 may perform clustering by a clustering method for setting the number of clusters. For example, the distribution device 100 clusters the division information of each mass MS by using the k-means method or the logistic regression method using the Dirichlet process, and generates a model using the division information for each cluster. In FIG. 2, the distribution device 100 will be described below assuming that the distribution device 100 clusters the division information of each mass MS by using the logistic regression method using the Dirichlet process and generates a model using the division information for each cluster. .. The details of the clustering method will be described later.

The distribution device 100 may target mass MS including a predetermined amount or more of division information as a model generation target in step S14. For example, the distribution device 100 does not target the mass MS corresponding to the context CT17 of the user U4 and the mass MS corresponding to the context CT11 of the user U2 that do not include the division information to be clustered. Further, for example, in the distribution device 100, the mass MS corresponding to the contexts CT13 and CT18 of the user U1 containing less than a predetermined amount (for example, two) of divided information may not be targeted for clustering.

In step S14, the distribution device 100 generates the model information MD11. The model information MD 11 includes information regarding clustering of divided information included in each mass MS as shown in the cluster information storage unit 124. Further, the model information MD 11 includes information about models M1 to M6 generated for each cluster as shown in the learning information storage unit 125.

In FIG. 2, the distribution device 100 classifies the divided information of each mass MS in the divided information storage unit 123 into six clusters (M1 to M6 in the cluster information storage unit 124). Note that M1 to M6 in the cluster information storage unit 124 indicate the cluster to which each division information belongs and the model to be generated. Further, in the mass MS in the cluster information storage unit 124, a model generated by a predetermined method is assigned to the mass MS whose symbols M1 to M6 are not shown. For example, the distribution device 100 may assign a model to a mass MS in the cluster information storage unit 124 to which a model is not assigned, based on a model assigned to another mass MS. For example, the distribution device 100 assigns the model M4 assigned to the mass MS corresponding to the context CT15 of the user U1 of the same male in his twenties to the mass MS corresponding to the context CT15 of the user U4 to which the model is not assigned. May be good.

Further, for example, in the distribution device 100, the model assigned to the mass MS corresponding to the context CT17 of the user U4 to which the model is not assigned is assigned to the mass MS corresponding to the context CT17 of the user U1 or the user U2 in the same 20s. You may assign a model based on. In this case, the distribution device 100 may assign a model that combines the model M2 assigned to the mass MS corresponding to the context CT17 of the user U1 and the model M1 assigned to the mass MS corresponding to the context CT17 of the user U2. Good. For example, the distribution device 100 has a weight of each feature of the model M2 assigned to the mass MS corresponding to the context CT17 of the user U1 and a weight of each feature of the model M1 assigned to the mass MS corresponding to the context CT17 of the user U2. A model in which and is synthesized at a predetermined ratio may be assigned. For example, the distribution device 100 assigns a model having a larger ratio of the model M2 assigned to the mass MS corresponding to the context CT17 of the male user U1 in his twenties to the mass MS corresponding to the context CT17 of the user U4. You may. For example, the distribution device 100 may assign the model M1-2 synthesized at a ratio of 0.3 for the model M1 and 0.7 for the model M2 to the mass MS corresponding to the context CT17 of the user U4. That is, the distribution device 100 generated the model M1- by adding the weight obtained by multiplying the weight of each feature of the model M1 by 0.3 and the weight obtained by multiplying the weight of each feature of the model M2 by 0.7. 2 may be assigned to the mass MS corresponding to the context CT17 of the user U4.

In FIG. 2, the distribution device 100 corresponds to the mass MS corresponding to the context CT11 of the user U1, the mass MS corresponding to the context CT15 of the user U2, the mass MS corresponding to the context CT17 of the user U2, and the context CT13 of the user U4. The division information included in the mass MS is classified into the same cluster. Further, it is shown that the distribution device 100 has generated the model M1 by using the union information that combines the division information included in the above four mass MSs. Further, as shown in the learning information storage unit 125, in the model information regarding the model M1, the weight of the feature 1 is "0.5", the weight of the feature 2 is "-0.4", and the weight of the feature 3 is "0. 2 ”etc.

Further, the distribution device 100 shares the divided information included in the mass MS corresponding to the context CT12 and CT17 of the user U1, the mass MS corresponding to the context CT14 of the user U2, and the mass MS corresponding to the context CT12 of the user U3 in the same cluster. Classify into. Further, the distribution device 100 indicates that the model M2 is generated by using the union information that combines the division information included in the above four mass MSs. Further, as shown in the learning information storage unit 125, in the model information regarding the model M2, the weight of the feature 1 is "-0.3", the weight of the feature 2 is "2.1", and the weight of the feature 3 is "-0". .2 ”etc.

As described above, in FIG. 2, the distribution device 100 classifies the division information of each mass MS in the division information storage unit 123 into six clusters (union information), and uses the information included in each cluster (union information). Models M1 to M6 are generated.

As described above, even if the distribution device 100 divides the behavior information of each division based on the context, the distribution device 100 suppresses the shortage of the amount of information used for generating each model by performing clustering across the divisions. can do. Further, the distribution device 100 can generate an appropriate model according to each context of each division by clustering the information divided based on the division and the context. Therefore, the distribution device 100 can appropriately estimate information about the user.

Note that FIG. 2 shows an example in which information regarding the presence or absence of the user's behavior in the distributed content is used as the behavior information, but what if the behavior information is the behavior performed by the user and can be collected? Information may be used. For example, the behavior information may be information related to the user's electronic commerce, or may be behavior related to the delivered advertisement (for example, click, conversion, etc.). In addition, the location is not limited to the home (home) or office (work location), and may be various information such as a waypoint during commuting, a frequently visited location, or moving.

[2-2. Delivery process]
Next, an example of the distribution process according to the embodiment will be described with reference to FIG. FIG. 3 is a diagram showing an example of the distribution process according to the embodiment. FIG. 3 shows a case where the user is the user U1. In FIG. 3, the case where the distribution device 100 has already acquired the user information regarding the user attribute of the user U1 and the user information such as the action history has been acquired will be described as an example.

First, the distribution device 100 acquires a content distribution request from the terminal device 10-1 used by the user U1 (step S15). In FIG. 3, in step S15, the terminal device 10-1 transmits information for identifying the user U1 (for example, a login ID or the like), position information indicating the location of the terminal device 10-1, or the like to the distribution device 100. The terminal device 10 acquires position information by a GPS (Global Positioning System) sensor or the like. In FIG. 3, it is assumed that the step S15 is performed in a situation where the time is 9:00 am and the location (position) of the terminal device 10-1 is the position LC12-1. The position LC12-1 or the like, which is information indicating the position and has a hyphen (“−”), means the position information within a predetermined range from the position LC12. Further, the positions LC11-1, LC11-2, etc. (see FIG. 7) mean position information within a predetermined range from the position LC11. That is, when the position LC12-1 indicates the position information of the user U1, it is estimated that the user U1 is at the work location at the position LC12.

The distribution device 100 that has acquired the content distribution request from the terminal device 10-1 determines a model for determining the content to be distributed to the terminal device 10-1 (step S16). The distribution device 100 estimates that the distribution request source is the user U1 based on the information for identifying the user U1 acquired from the terminal device 10-1. Then, the distribution device 100 estimates the location of the user U1 based on the user information of the user U1 stored in the user information storage unit 121 and the position LC12-1 of the terminal device 10-1. In FIG. 3, as shown in the user information storage unit 121, the work location of the user U1 is the position LC12, and the position of the terminal device 10-1 is the position LC12-1. Therefore, in the distribution device 100, the user U1 is in the office ( Estimated to be at work location).

Then, since the time when the distribution request is made in step S15 is 9:00 am, the distribution device 100 estimates that the context is the context CT12 corresponding to the office in the morning. After that, the distribution device 100 determines the model M2 to be used for determining the content to be distributed based on the information indicating the model M2 stored in the mass MS corresponding to the user U1 and the context CT12 in the learning information storage unit 125. To do.

For example, the distribution device 100 calculates the score of each content stored in the content information storage unit 126 (see FIG. 11) using the determined model M2. Specifically, the distribution device 100 includes character information and image information such as titles and article texts of each content CN11 (article A) to content CN18 (article H) stored in the content information storage unit 126, and model M2. Is used to calculate the scores of each content CN11 to CN18 and the like. In FIG. 3, the distribution device 100 calculates the scores of the contents CN11 to CN18 and the like as shown in the contents list CL11. For example, the distribution device 100 calculates the score of the content CN11 which is the article A as "3.6". Further, for example, the distribution device 100 calculates the score of the content CN12, which is the article B, as “2.5”.

Then, the distribution device 100 determines the ranking of each content CN11 to CN18 or the like based on the score shown in the content list CL11 (step S17). In FIG. 3, the distribution device 100 determines the order of the contents CN11 to CN18 and the like in order from the one with the highest score shown in the contents list CL11. Specifically, the distribution device 100 determines the ranking of the article E, which is the content CN15 having the highest score, to be first. Further, the distribution device 100 determines the ranking of the article G, which is the content CN17 having the highest score next to the article E, to be second. As a result, in the distribution device 100, as shown in the ranking list LL11, the ranking 1st place is the article E, the ranking 2nd place is the article G, the ranking 3rd place is the article A, and the ranking 4th place is the article F. Decide that there is.

After that, the distribution device 100 distributes the content shown in the ranking list LL11 to the terminal device 10-1 (step S18). Specifically, the contents displayed in the order of article E, article G, article A, article F, and the like are distributed to the terminal device 10-1. Then, the terminal device 10-1 that has received the content shown in the ranking list LL11 displays the content (step S19). In FIG. 3, the terminal device 10-1 displays the article E, the article G, the article A, and the article F, which are the contents having the higher rank among the contents shown in the rank list LL11. The user U1 who uses the terminal device 10-1 touches the screen of the terminal device 10-1 and performs a scroll operation or the like to sequentially display the article B, the article H, and the like below from the article F.

As described above, the distribution device 100 determines the model according to the context when the distribution request is acquired. Then, the distribution device 100 determines the ranking of the contents based on the score of the contents calculated using the determined model. As a result, the distribution device 100 can distribute appropriate content according to the user and the context at the time of the distribution request. Therefore, the distribution device 100 can extract appropriate contents. In the above example, the distribution device 100 calculates the score of each content using the model selected based on the context of the user, and ranks the calculated contents in order from the highest score. .. As described above, in the above-described example, the distribution device 100 shows a case where the distribution device 100 uses a model that calculates the possibility of viewing each content as a continuous numerical value (score) in the corresponding context. That is, in the above example, the higher the score of each content calculated by the distribution device 100 using the model selected based on the user's context, the higher the possibility that the content will be viewed. , The distribution device 100 may make any prediction using the selected model.

For example, the distribution device 100 may predict that the user will browse the content having a score equal to or higher than a predetermined threshold value, and predict that the user will not browse the content having a score lower than the predetermined threshold value. In this way, the distribution device 100 may or may not allow the user to browse the content (for example, the value "1") based on the score of each content calculated using the model selected based on the context (for example, the value "0"). ”) The binary prediction may be performed. The distribution device 100 is not limited to the binary prediction, but may generate a model for predicting continuous values and discrete values, and predict continuous values and discrete values based on the generated model. For example, the distribution device 100 may make a prediction corresponding to a so-called multi-label problem in which not only two values of "0" and "1" but also three or more values are handled. For example, the distribution device 100 may predict at which stage of three or more stages the user is present, instead of the two stages of whether or not the user browses the content. For example, the distribution device 100 may use a plurality of threshold values to predict at which stage the user is likely to browse the content. Specifically, the prediction device 100 uses a first threshold value and a second threshold value smaller than the first threshold value, and when the score is equal to or higher than the first threshold value, the user may browse the content. If it is determined to be high and the score is below the first threshold and is greater than or equal to the second threshold, it is determined that the user is likely to view the content moderately, and if the score is below the second threshold, the content is determined to be high. May be predicted to be less likely to be viewed by the user. Further, for example, the distribution device 100 may generate a model for predicting the number of reactions or predicting the score of a predetermined target, and make a prediction based on the generated model. That is, any prediction may be made using the model selected by the distribution device 100 based on the user's context. Further, the distribution device 100 may generate a model according to the target to be predicted as described above.

Further, in the example shown in FIG. 3, the user U1 did not select (click) the article E, the article G, the article A, and the article F displayed on the terminal device 10-1, that is, in the context CT12 of the user U1. It will be described below assuming that the prediction accuracy using the model M2 is low.

[2-3. Advertisement distribution using interest information]
Next, an example of the distribution process according to the embodiment will be described with reference to FIG. FIG. 4 is a diagram showing an example of advertisement distribution processing based on the estimated user interest according to the embodiment. Further, FIG. 4 shows a case where the distribution device 100 provides information to another service. Specifically, FIG. 4 shows a case where the distribution device 100 distributes interest information to the advertising device 50 that provides the advertisement distribution service, and the advertising device 50 that has acquired the interest information distributes the advertisement based on the interest information. .. In FIG. 4, it is assumed that the distribution system 1 includes the advertising device 50. Further, in FIG. 4, a case where the advertisement device 50 delivers the advertisement to the terminal device 10-3 of the user U3 based on the interest information acquired from the distribution device 100 will be described as an example. Further, FIG. 4 shows a case where the advertising device 50 has information indicating that the office (work location) of the user U3 is the position LC32.

First, the distribution device 100 provides the information to the advertising device 50 by delivering the interest information to the advertising device 50 (step S21). In FIG. 4, the distribution device 100 distributes the interest information ES11 regarding the user U3. The interest information ES11 shown in FIG. 4 is the same as the interest information ES11 shown in FIG. In addition, the interest information ES11 includes interest information ES11-1 indicating an overall interest over the entire context of the user U3 and interest information ES11-2 indicating an interest in each context of the user U3. For example, the interest information ES11-1 is interest information indicating that the user U3 is highly interested in the economy as a whole. Further, for example, the interest information ES11-2 is interest information indicating that the user U3 is highly interested in the economy when he is in the office and is highly interested in sports when he is at home (home) in the morning. ..

The advertising device 50 that has acquired the interest information ES 11 from the distribution device 100 acquires an advertisement request requesting the distribution of the advertisement from the terminal device 10-3 used by the user U3 (step S22). In FIG. 4, it is assumed that step S22 is performed in a situation where the time is 12:00 noon and the location (position) of the terminal device 10-3 is position LC32-1.

The advertising device 50 that has acquired the advertisement request from the terminal device 10-3 selects the advertisement to be delivered to the terminal device 10-3 (step S23). At this time, the advertising device 50 uses the interest information ES11 acquired from the distribution device 100 to select an advertisement to be distributed to the terminal device 10-3 of the user U3. As shown in FIG. 4, the interest information ES11 includes interest information ES11-2 indicating that the user U3 is highly interested in the economy when he / she is in the office. Further, as described above, the advertising device 50 has information indicating that the office (work location) of the user U3 is at the position LC32. Further, the position LC32-1 means the position information within a predetermined range from the position LC32. That is, when the position LC32-1 indicates the position information of the user U3, the advertising device 50 estimates that the user U3 is at the work location at the position LC32.

Then, the advertising device 50 is related to the economy as corresponding to the interest information ES11-2 indicating that the advertisement is requested in step S22 at 12:00 noon and the place is the office. The advertisement is selected as the advertisement to be delivered to the terminal device 10-3. For example, the advertising device 50 selects the advertisement AC11 related to FX (Foreign Exchange: foreign exchange margin trading) as an advertisement to be delivered to the terminal device 10-3.

After that, the advertising device 50 delivers the advertising AC11 to the terminal device 10-3 (step S24). Then, the terminal device 10-1 that has received the advertisement AC11 displays the advertisement AC11 (step S25).

As described above, the distribution device 100 shows a case where information is provided to another service. For example, the distribution device 100 distributes interest information to the advertisement device 50 that provides the advertisement distribution service. Then, the advertising device 50 that has acquired the interest information ES11 from the distribution device 100 distributes the advertisement based on the interest information ES11. As a result, the distribution system 1 can appropriately distribute the advertisement based on the interest information estimated by the distribution device 100. In the above-mentioned example, the advertisement device 50 delivers the advertisement, but the advertisement may be delivered based on the interest information estimated by the delivery device 100.

[3. Distribution device configuration]
Next, the configuration of the distribution device 100 according to the embodiment will be described with reference to FIG. FIG. 5 is a diagram showing a configuration example of the distribution device according to the embodiment. As shown in FIG. 5, the distribution device 100 includes a communication unit 110, a storage unit 120, and a control unit 130. The distribution device 100 has an input unit (for example, a keyboard, a mouse, etc.) that receives various operations from the administrator of the distribution device 100, and a display unit (for example, a liquid crystal display, etc.) for displaying various information. You may.

(Communication unit 110)
The communication unit 110 is realized by, for example, a NIC (Network Interface Card) or the like. Then, the communication unit 110 is connected to the network by wire or wirelessly, and transmits / receives information to / from the terminal device 10.

(Memory 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 (Flash Memory), or a storage device such as a hard disk or an optical disk. As shown in FIG. 5, the storage unit 120 according to the embodiment includes a user information storage unit 121, an action information storage unit 122, a division information storage unit 123, a cluster information storage unit 124, and a learning information storage unit 125. , Content information storage unit 126, and interest information storage unit 127.

(User information storage unit 121)
The user information storage unit 121 according to the embodiment stores various information about the user. For example, the user information storage unit 121 stores various information related to user attributes. FIG. 6 is a diagram showing an example of a user information storage unit according to the embodiment. The user information storage unit 121 shown in FIG. 6 includes items such as "user ID", "age", "gender", "home", "work location", and "interest".

The "user ID" indicates identification information for identifying a user. For example, the user identified by the user ID "U1" corresponds to the user U1 shown in the example of FIG. Further, "age" indicates the age of the user identified by the user ID. The "age" may be the specific age of the user identified by the user ID, such as 35 years old. Further, "gender" indicates the gender of the user identified by the user ID.

Further, "home" indicates the location information of the user's home identified by the user ID. In the example shown in FIG. 6, "home" is illustrated with an abstract code such as "LC11", but may be information indicating latitude or longitude. Further, for example, "home" may be an area name or an address.

Further, the "work location" indicates the location information of the user's work location identified by the user ID. In the example shown in FIG. 6, "work location" is shown by an abstract code such as "LC12", but may be information indicating latitude or longitude. Further, for example, the "work location" may be a region name or an address.

Further, "interest" indicates the interest of the user identified by the user ID. That is, "interest" indicates an object that the user identified by the user ID is highly interested in. In the example shown in FIG. 6, one "interest" is shown for each user, but there may be a plurality of "interests".

For example, in the example shown in FIG. 6, the age of the user identified by the user ID "U1" is "20's", and the gender is "male". Further, for example, the user identified by the user ID "U1" indicates that his / her home is "LC11". Further, for example, the user identified by the user ID "U1" indicates that the work location is "LC12". Further, for example, the user identified by the user ID "U1" indicates that he / she is interested in "sports".

The user information storage unit 121 is not limited to the above, and may store various information depending on the purpose. For example, the user information storage unit 121 may store information such as a name, family structure, and income.

(Behavior information storage unit 122)
The behavior information storage unit 122 according to the embodiment stores various information related to the user's behavior. FIG. 7 is a diagram showing an example of the behavior information storage unit according to the embodiment. For example, the behavior information storage unit 122 stores the user's behavior information for the content delivered to the terminal device 10 of each user. The action information storage unit 122 shown in FIG. 7 includes items such as "user ID", "behavior", and "situation". In addition, the "action" includes items such as "action content" and "presence / absence". In addition, the "situation" includes items such as "time" and "position".

The "user ID" indicates identification information for identifying a user. For example, the user identified by the user ID "U1" corresponds to the user U1 shown in the example of FIG. Further, "behavior" indicates information about the behavior of the corresponding user. In addition, "status" indicates information regarding the status of the corresponding user.

“Behavior content” indicates identification information for identifying each action. For example, the action (action AT11) identified by the action content "AT11" corresponds to the user's action (for example, clicking the content) with respect to the content (for example, content CN11) delivered to the user. The "action content" is not limited to the identification information, and a specific action content may be stored. In addition, "presence / absence" indicates the presence / absence of the corresponding action content. When "presence / absence" is "1", it indicates that the user has performed the corresponding action. Further, when "presence / absence" is "0", it indicates that the user did not perform the corresponding action.

"Time" indicates the time for each action. The time "TM11" indicates the time related to the action AT11. For example, the time "TM11" may be the time when the content (for example, the content CN11) is delivered to the user. Further, for example, the time "TM11" may be the time when the user clicks the content (for example, the content CN11) delivered to the user. The time “TM12” related to the action (action AT12) identified by the action content “AT12” which is “0” may be, for example, the time when the content is delivered to the user. Further, for example, the time "TM12" may be a time after a predetermined time has elapsed without being clicked after the content is delivered to the user.

In addition, "position" indicates a position related to each action. The position "LC11-1" indicates the position regarding the action AT11. For example, the position "LC11-1" may be the position information when the user requests the distribution of the content. Further, for example, the position "LC11-1" may be the position information when the user clicks the delivered content.

For example, in the example shown in FIG. 7, the user identified by the user ID “U1” has an action (action AT13) identified by the action content “AT13” with or without “1”, and has performed the action AT13. Shown. Further, the user identified by the user ID "U1" indicates that the time related to the action AT13 is the time "TM13" and the position is the position "LC11-3".

The behavior information storage unit 122 is not limited to the above, and may store various information depending on the purpose. For example, the behavior information storage unit 122 may store information regarding the time from when the content is delivered until the user acts. Further, although FIG. 7 shows a case where the action information is stored in the action information storage unit 122 for each user ID, the action information is not limited to each user ID and may be stored, for example, in chronological order.

(Divided information storage unit 123)
The divided information storage unit 123 according to the embodiment stores various information related to the divided information. FIG. 8 is a diagram showing an example of the divided information storage unit according to the embodiment. The divided information storage unit 123 shown in FIG. 8 stores the action information divided based on the “division” and the “context”. Specifically, the action information corresponding to each "context" is stored for each "classification" of the user. For example, the information stored in the divided information storage unit 123 is generated from the behavior information stored in the behavior information storage unit 122.

“Category” indicates a classification related to the user. FIG. 8 shows users U1 to U4, that is, a case where each user is a division. The "classification" is not limited to each user and may be an abstract user classification or the like, but this point will be described later. Further, "context" indicates a context. FIG. 8 shows a context based on time zone and position for simplicity. Specifically, the "context" consists of four time zones: morning (4 to 10 o'clock), noon (10 to 16 o'clock), night (16 to 22 o'clock), midnight (22 to 4 o'clock), and home (22:00 to 4 o'clock). Eight contexts CT11-CT18 are shown based on two locations, home) or office (work location). In addition, "○" shown on the right side of the action content such as "AT11" in each cell shown in FIG. 8 corresponds to "1" of presence / absence, and "x" corresponds to "0" of presence / absence. That is, in the case of "○", it indicates that the user has performed the corresponding action. Further, in the case of "x", it indicates that the user did not perform the corresponding action.

For example, in the example shown in FIG. 8, the user U1 indicates that the action AT11 is “◯” in the context CT11 and the action AT11 is performed at home (home) in the time zone “morning”. Further, the user U1 indicates that the action AT11 is “x” in the context CT12, and the action AT11 is not performed in the office (work location) in the time zone “morning”.

The divided information storage unit 123 is not limited to the above, and various information may be stored depending on the purpose. Further, in FIG. 8, for the sake of explanation, a case where information regarding the action content and presence / absence of each action information is stored in the divided information storage unit 123 is illustrated, but the divided information storage unit 123 stores the information in the action information storage unit 122. Information on which cell the action information is given corresponds to may be stored.

(Cluster information storage unit 124)
The cluster information storage unit 124 according to the embodiment stores various information regarding the combination of the divided information. FIG. 9 is a diagram showing an example of the cluster information storage unit according to the embodiment. The cluster information storage unit 124 shown in FIG. 9 stores information indicating how the division information of each cell shown in the division information storage unit 123 in FIG. 8 is combined and information identifying a model corresponding to each combination. Will be done. Specifically, information for identifying the model corresponding to each cell according to the user's "classification" and "context" is stored. For example, the information stored in the cluster information storage unit 124 is generated by a process of generating a model.

“Category” indicates a classification related to the user. FIG. 9 shows a case where users U1 to U4, that is, each user is a division, as in FIG. Further, "context" indicates a context. FIG. 9 shows a context based on time zone and position, as in FIG. Specifically, the "context" consists of four time zones: morning (4 to 10 o'clock), noon (10 to 16 o'clock), night (16 to 22 o'clock), midnight (22 to 4 o'clock), and home (22:00 to 4 o'clock). Eight contexts CT11-CT18 are shown based on two locations, home) or office (work location). In addition, "M1", "M2", etc. in each cell shown in FIG. 9 indicate information for identifying a model generated by union information combining division information of cells in which the same code is stored.

For example, in the example shown in FIG. 9, the model identified by "M1" (hereinafter, may be referred to as "model M1"; the same applies to the other "M2" to "M6") is the context CT11 of the user U1. From the union information that combines the mass MS corresponding to the mass MS corresponding to the user U2, the mass MS corresponding to the context CT15 of the user U2, the mass MS corresponding to the context CT17 of the user U2, and the mass MS corresponding to the context CT13 of the user U4. Indicates that it has been generated. Further, for example, the model M2 combines the division information included in the mass MS corresponding to the context CT12 and CT17 of the user U1, the mass MS corresponding to the context CT14 of the user U2, and the mass MS corresponding to the context CT12 of the user U3. Indicates that it was generated from the union information.

The cluster information storage unit 124 is not limited to the above, and may store various information depending on the purpose.

(Learning information storage unit 125)
The learning information storage unit 125 according to the embodiment stores information related to learning. For example, the learning information storage unit 125 stores the model information generated by the generation process. FIG. 10 is a diagram showing an example of the learning information storage unit according to the embodiment. The learning information storage unit 125 shown in FIG. 10 has items such as "feature 1" to "feature 3" corresponding to each model M1 to M6 as model information.

For example, in the example shown in FIG. 10, the model information regarding the model M1 is such that the weight of the feature 1 is "0.5", the weight of the feature 2 is "-0.4", the weight of the feature 3 is "0.2", and the like. Indicates that. For example, when the features (features) of the model are represented by an m-dimensional vector, the number of features is m, and the weights of features 1 to m are stored.

The learning information storage unit 125 is not limited to the above, and may store various model information depending on the purpose.

(Content information storage unit 126)
The content information storage unit 126 according to the embodiment stores various information related to the content (article). FIG. 11 is a diagram showing an example of the content information storage unit according to the embodiment. The content information storage unit 126 shown in FIG. 11 has items such as "content ID", "content", and "category".

The "content ID" indicates identification information for identifying the content. "Content" refers to an article that is content acquired from a provider such as a so-called content provider. In FIG. 11, an example in which conceptual information such as "article A" is stored in "content" is shown, but in reality, character information, a combination of character information and an image, or a file showing the storage location thereof is shown. The path name etc. are stored. Further, "category" indicates classification information based on the content.

For example, in the example shown in FIG. 11, the article A (content CN11) identified by the content ID “CN11” is classified into the category “economy”. Further, in the example shown in FIG. 11, the article B (content CN12) identified by the content ID “CN12” is classified into the category “sports”.

The content information storage unit 126 is not limited to the above, and various information may be stored depending on the purpose. For example, the content information storage unit 126 may store information regarding the date and time when the content was acquired and the date and time when the content was created. The content information storage unit 126 may store information regarding the evaluation value of each provider. Further, the content information storage unit 126 may store identification information for identifying the content provider. Further, the content information storage unit 126 may store the score of the content calculated by each model.

(Interest information storage unit 127)
The interest information storage unit 127 according to the embodiment stores various information regarding the user's interest. FIG. 12 is a diagram showing an example of the interest information storage unit according to the embodiment. For example, the interest information storage unit 127 stores a score indicating information regarding interest in each object (classification) for each user. Specifically, the interest information storage unit 127 stores an interest score indicating interest in each object (classification) for each user. The interest information storage unit 127 shown in FIG. 12 includes items such as “user ID”, “target”, and “interest score”. Further, the "interest score" includes items such as "CT11" to "CT18" and "total" corresponding to each context.

The "user ID" indicates identification information for identifying a user. For example, the user identified by the user ID "U1" corresponds to the user U1 shown in the example of FIG. Further, "target" indicates a target (classification) for calculating the user's interest score. Further, the "interest score" indicates a numerical value relating to the degree of interest of the corresponding user.

For example, in the example shown in FIG. 12, the user identified by the user ID "U1" (ie, user U1) indicates that the interest score for the target "politics" in the context "CT11" is "0.5". .. Further, the user U1 indicates that the interest score for the target "economy" in the context "CT11" is "0.2". Further, the user U1 indicates that the interest score for the target "sports" in the context "CT11" is "1.2".

For example, in the example shown in FIG. 12, the user identified by the user ID "U3" (ie, user U3) indicates that the total interest score for the target "politics" is "-1.8". The user U3 also indicates that the total interest score for the target "economy" is "8.1". User U3 also indicates that the total interest score for the target "sports" is "1.4".

The interest information storage unit 127 is not limited to the above, and may store various information depending on the purpose. For example, the interest information storage unit 127 may store information indicating the interest in each object in each context or total in order. Further, FIG. 12 shows a case where interest information is stored in the interest information storage unit 127 for each user ID, but is not limited to each user ID, for example, for a predetermined user classification (for example, for men in their twenties and sports). Interest information for each interested user, etc.) may also be stored.

(Control unit 130)
Returning to the description of FIG. 5, the control unit 130 is an example of various programs (an example of a distribution program) stored in a storage device inside the distribution device 100 by, for example, a CPU (Central Processing Unit) or an MPU (Micro Processing Unit). Is realized by executing RAM as a work area. Further, the control unit 130 is realized by, for example, an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

As shown in FIG. 5, the control unit 130 includes an acquisition unit 131, an estimation unit 132, a generation unit 133, a determination unit 134, and a distribution unit 135, and functions and operations of information processing described below. To realize or execute. The internal configuration of the control unit 130 is not limited to the configuration shown in FIG. 5, and may be another configuration as long as it is a configuration for performing information processing described later. Further, the connection relationship of each processing unit included in the control unit 130 is not limited to the connection relationship shown in FIG. 5, and may be another connection relationship.

(Acquisition unit 131)
The acquisition unit 131 acquires the user's behavior information. The acquisition unit 131 acquires the user's action information from the terminal device 10 used by the user. In addition, the acquisition unit 131 acquires user information about the user. In addition, the acquisition unit 131 acquires the division information which is the behavior information regarding the user's behavior for each division regarding the user and which is the behavior information divided based on the context. For example, the acquisition unit 131 acquires the division information from the division information storage unit 123. In addition, the acquisition unit 131 acquires the division information in which the behavior information regarding the behavior of the user or the user for each classification based on the user attribute is divided based on the context as the division.

In addition, the acquisition unit 131 acquires a content distribution request from the terminal device 10. In addition, the acquisition unit 131 acquires information that identifies the user (for example, login ID, terminal identification information, etc.), location information of the terminal device 10, and the like.

In addition, the acquisition unit 131 combines the behavior information related to the behavior of each user, which is the behavior information divided based on the context, based on the tendency of the behavior information included in each of the division information. Information about a model that predicts a predetermined target, which is generated for each union information, which is information including combinations across users. For example, the acquisition unit 131 acquires information about the model from the cluster information storage unit 124 and the learning information storage unit 125.

(Estimation unit 132)
The estimation unit 132 estimates various information from the various acquired information. For example, the estimation unit 132 estimates various information based on the time when the content is delivered and the user's position information acquired from the terminal device 10. For example, the estimation unit 132 estimates the user of the distribution request source from the information for identifying the user acquired from the terminal device 10. For example, the estimation unit 132 estimates the location of the user based on the user information of the user stored in the user information storage unit 121 and the position of the terminal device 10. In FIG. 3, in the estimation unit 132, as shown in the user information storage unit 121, the work location of the user U1 is the position LC12, and the position of the terminal device 10-1 is the position LC12-1. Therefore, the user U1 is in the office ( Estimated to be at work location). Further, in FIG. 3, since the delivery request is made at 9:00 am, the estimation unit 132 estimates the context as the context CT12 corresponding to the office in the morning.

In addition, the estimation unit 132 uses the user based on the degree of similarity between the union information including each division information of the user and the union information including each division information of the other division when one division is a user. User attributes may be estimated.

Further, the estimation unit 132 estimates the information about the user based on the information about the model acquired by the acquisition unit 131. The estimation unit 132 estimates the interest of one user based on the allocation of the model for each context in one user and the information about the model. The estimation unit 132 estimates the interest of one user in the one context based on the allocation of the model of one context in one user and the information about the model. For example, the estimation unit 132 estimates the information about the user as the information about the model based on the weight of the features included in each model.

For example, the estimation unit 132 calculates the interest score based on the information about the model. In FIG. 1, the estimation unit 132 calculates the interest score in each context for each user by using the model information MD11. For example, the estimation unit 132 calculates the interest score for each target context for each user, as shown in the interest information storage unit 127. For example, for example, the estimation unit 132 calculates the interest score of the user U1 in the context CT11 for each object based on the weight of each feature of the model M1 assigned to the mass MS corresponding to the context CT11 of the user U1. Further, for example, the estimation unit 132 estimates information about the user based on the calculated interest score.

In FIG. 1, the estimation unit 132 estimates the interest of the user U3 as shown in the interest information ES11. For example, the estimation unit 132 estimates the interest information ES11-1 indicating the overall interest of the user U3 over the entire context. For example, the estimation unit 132 estimates that the user U3 as a whole is highly interested in the economy.

Further, in FIG. 1, the estimation unit 132 estimates interest information ES11-2 indicating interest in each context of the user U3. For example, the estimation unit 132 estimates that the user U3 is highly interested in the economy when he is in the contexts CT12, CT14, CT16, CT18, that is, in the office. Further, for example, the estimation unit 132 estimates that the user U3 is highly interested in sports when the user U3 is at the context CT11, that is, at home (home) in the morning.

Further, the estimation unit 132 estimates a user similar to one user based on the allocation of the model for each context in one user, the allocation of the model for each context in another user, and the information about the model. For example, the estimation unit 132 estimates a user similar to one user based on the pattern of model allocation for each context in one user and the pattern of model allocation for each context in another user. The details of these points will be described later.

Further, in the estimation unit 132, when behavior information about a user identified as one user is collected from a plurality of terminal devices 10, one user is identified as one user based on the information about the model. It is estimated whether the user who uses the terminal device 10 and the user who uses the other terminal device 10 are different users. The distribution device 100 identifies each terminal device 10 by information that identifies the terminal device 10 such as an individual identification number acquired from each terminal device 10 when collecting action information. For example, when the user is identified by information such as a login ID, the estimation unit 132 may divide the identifier and the collected action information as different users even if the login IDs are the same. For example, when the estimation unit 132 authenticates with the same login ID in a plurality of terminal devices 10, the estimation unit 132 may recognize each terminal device 10 as a different user and divide the action information. For example, when the estimation unit 132 authenticates with the same login ID in a plurality of terminal devices 10, the estimation unit 132 is estimated from a model corresponding to the behavior information collected from one terminal device 10 and other objects of high interest. When the target of high interest estimated from the model corresponding to the behavior information collected from the terminal device 10 is significantly different, the user who uses one terminal device 10 and the user who uses the other terminal device 10 are different. It may be presumed to be another user. Then, the estimation unit 132 may recognize each terminal device 10 as a different user and divide the action information.

Further, the estimation unit 132 estimates whether the behavior information of one user includes the behavior information of another user based on the allocation of the model for each context in one user and the information about the model. For example, in the estimation unit 132, when the allocation of the model for each context in one user is significantly different between the contexts CT11 to CT14 in the morning to noon and the contexts CT15 to CT18 in the night to midnight, the morning to It may be presumed that the user in the daytime contexts CT11 to CT14 and the user in the night to midnight contexts CT15 to CT18 are different users.

(Generator 133)
The generation unit 133 may generate the division information by dividing the collected behavior information regarding the user's behavior based on the context. Further, the generation unit 133 may store the generated division information in the division information storage unit 123. For example, the generation unit 133 may divide the collected information regarding the behavior of each user. For example, the generation unit 133 divides the collected behavior information of each user based on the context estimated by the estimation unit 132.

The generation unit 133 generates a model for predicting a predetermined target for each union information which is the division information combined based on the tendency of the behavior information included in each division information. For example, in FIG. 2, the mass MS at the position where the user U1 and the context CT11 intersect and the mass MS at the position where the user U4 and the context CT13 intersect are classified into the same cluster. The mass MS at the position where the user U1 and the context CT11 intersect and the mass MS at the position where the user U4 and the context CT13 intersect both include division information in which the action AT11 is “◯”. The mass MS at the position where the user U1 and the context CT11 intersect, and the mass MS at the position where the user U4 and the context CT13 intersect include division information showing the same tendency. Then, the generation unit 133 uses the union information including the division information of the mass MS at the position where the user U1 and the context CT11 intersect and the division information of the mass MS at the position where the user U4 and the context CT13 intersect, and the model M1 To generate.

In addition, the generation unit 133 generates a model for predicting a predetermined target regarding the user included in the division corresponding to the division information in the context corresponding to each of the division information included in the union information. The generation unit 133 generates a model for predicting a predetermined target for each union information including a combination straddling the divisions. For example, the generation unit 133 uses the union information including the division information of the mass MS at the position where the user U2 and the context CT12 intersect and the division information of the mass MS at the position where the user U4 and the context CT14 intersect, and the model M6 To generate. That is, the generation unit 133 generates the model M6 by using the union information straddling the divisions between the user U2 and the user U4.

For example, the generation unit 133 determines for each union information in which the division information is combined so that the prediction accuracy of the predetermined target in the combination of the user classification and the context corresponding to each division information included in the union information becomes high. Generate a model that predicts the target. For example, the generation unit 133 generates a model for predicting a predetermined target for each union information in which division information having similar behavior information included in each division information is combined. For example, the generation unit 133 generates a model that predicts the user's interest in the content as a predetermined target.

For example, the generation unit 133 generates a model for predicting a predetermined target for each union information in which the division information is combined by a method in which the number of union information becomes a predetermined number. For example, the generation unit 133 generates a model for each union information in which the division information is combined by using the k-means method. In this case, the generation unit 133 generates the union information by combining the division information so as to be the number of clusters (the number of union information) determined (set) by the determination unit 134, and generates a model for each union information.

For example, the generation unit 133 generates a model for predicting a predetermined target for each union information in which the division information is combined by a method in which the number of union information can fluctuate. For example, the generation unit 133 generates a model for each union information in which the division information is combined by using the logistic regression method using the Dirichlet process. For example, the generation unit 133 generates union information by sequentially combining the division information, and generates a model for each union information.

For example, the generation unit 133 generates union information by combining a plurality of division information, and generates a model for the union information. For example, the generation unit 133 randomly selects a plurality of division information to generate union information, and generates a model for the union information. Then, the generation unit 133 verifies the prediction accuracy of the generated model. The prediction accuracy here means the probability of predicting the presence or absence of user behavior. For example, the prediction accuracy referred to here means the probability that a predetermined score or higher will be calculated for the content that is "○" in the behavior information when the model is used. Then, when the prediction accuracy of the generated model is equal to or higher than a predetermined threshold value, the generation unit 133 may perform a process of maintaining the combination and further combining the division information. Further, the generation unit 133 may use a predetermined hyperparameter such as a penalty term. For example, the generation unit 133 may use a penalty term whose value decreases as the number of clusters decreases. For example, the generation unit 133 may determine whether to maintain or cancel the combination based on a numerical value calculated by subtracting a value obtained by multiplying the number of clusters by a predetermined coefficient from the prediction accuracy of the model. In this case, the generation unit 133 generates union information and a model classified into a predetermined number of clusters based on a trade-off between the decrease in the number of clusters and the prediction accuracy of the model.

For example, the generation unit 133 may verify the prediction accuracy of the generated model by cross-validating a part of the model for generation and the rest for verification. For example, the generation unit 133 may use 80% of the union information for model generation and the remaining 20% for verification. For example, when the verified prediction accuracy satisfies a predetermined criterion, the generation unit 133 may perform a process of maintaining the combination and further combining the division information. Further, when the verified prediction accuracy does not satisfy a predetermined criterion, the generation unit 133 may release the combination and perform a process of combining another division information. In addition, the generation unit 133 maintains or cancels the combination by averaging cross-validation by exchanging the information used for model generation and the information used for verification each time the division information is combined. May be determined.

For example, in the generation unit 133, when one division is a user and the division information of the user in one context does not satisfy a predetermined condition, the generation unit 133 divides another division similar to the user attribute of the user in one context. A model is generated by adding information to the user's split information in one context. For example, when the amount of division information of the user in one context is less than a predetermined amount, the generation unit 133 adds the division information of the other division in one context to the division information of the user in one context, thereby performing a model. To generate. The details of these points will be described later.

(Decision unit 134)
The determination unit 134 determines a model for determining the content to be delivered to the terminal device 10. In FIG. 3, the determination unit 134 uses a model M2 to determine the content to be distributed based on the information indicating the model M2 stored in the mass MS corresponding to the user U1 and the context CT12 in the cluster information storage unit 124. To decide.

For example, the determination unit 134 calculates the score of the content based on the determined model and the content. Further, for example, the determination unit 134 determines the ranking of each content based on the calculated score. In FIG. 3, the determination unit 134 determines the ranking of each content CN11 to CN18 or the like based on the score shown in the content list CL11. In addition, the determination unit 134 determines the ranking of the article E, which is the content CN15 having the highest score, to be the first. In addition, the determination unit 134 determines the ranking of the article G, which is the content CN17 having the highest score next to the article E, to be second. As a result, as shown in the ranking list LL11, the determination unit 134 has the ranking 1st place is the article E, the ranking 2nd place is the article G, the ranking 3rd place is the article A, and the ranking 4th place is the article F. Decide that there is.

Further, the determination unit 134 determines that the model assigned to the context is assigned to the context in which the model is not assigned in one user, and the model assigned to the context is assigned to the user similar to the user. The details of this point will be described later.

(Distribution unit 135)
The distribution unit 135 distributes various information to the terminal device 10. For example, the distribution unit 135 distributes the content to the terminal device 10. In addition, the distribution unit 135 distributes the content determined by the determination unit 134. In FIG. 3, the distribution unit 135 distributes the content shown in the ranking list LL11 to the terminal device 10-1. Further, the distribution unit 135 distributes the contents displayed in the order of article E, article G, article A, article F, etc. to the terminal device 10-1. Further, in FIG. 4, the distribution unit 135 distributes the estimated information regarding the user's interest to the advertising device 50.

[4. Flow of generation process]
[4-1. Method without setting the number of clusters]
Next, the procedure of the generation process by the distribution system 1 according to the embodiment will be described with reference to FIG. FIG. 13 is a flowchart showing an example of the generation process according to the embodiment. Specifically, FIG. 13 is a flowchart showing an example of generation processing by a method that does not determine the number of clusters (number of union information).

As shown in FIG. 13, the acquisition unit 131 of the distribution device 100 acquires the action information of each user (step S101). For example, the acquisition unit 131 acquires user behavior information from the terminal device 10 used by each user.

Further, the generation unit 133 of the distribution device 100 divides the information according to the context (step S102). For example, the generation unit 133 generates the divided information by dividing the action information according to the context when the action information is acquired. In addition, step S101 and step S102 may be performed a plurality of times.

After that, the generation unit 133 clusters the division information and generates a model by a method that does not determine the number of clusters (step S103). For example, the generation unit 133 generates a model for each union information in which the division information is combined by using the logistic regression method using the Dirichlet process.

[4-2. Method to set the number of clusters]
Next, the procedure of the generation process by the distribution system 1 according to the embodiment will be described with reference to FIG. FIG. 14 is a flowchart showing an example of the generation process according to the embodiment. Specifically, FIG. 14 is a flowchart showing an example of a generation process by a method of presetting (determining) the number of clusters (number of union information).

As shown in FIG. 14, the acquisition unit 131 of the distribution device 100 acquires the action information of each user (step S201). For example, the acquisition unit 131 acquires user behavior information from the terminal device 10 used by each user.

In addition, the generation unit 133 of the distribution device 100 divides the information according to the context (step S202). For example, the generation unit 133 generates the divided information by dividing the action information according to the context when the action information is acquired. In addition, step S201 and step S202 may be performed a plurality of times.

Further, the determination unit 134 of the distribution device 100 sets (determines) the number of clusters (the number of union information) (step S203). For example, the determination unit 134 sets (determines) the number of clusters (number of union information) to "6". The process of step S203 may be performed at any timing as long as it is before step S204.

After that, the generation unit 133 clusters the division information and generates a model by a method of determining the number of clusters (step S204). For example, the generation unit 133 generates a model for each union information in which the division information is combined by using the k-means method.

[5. Delivery processing flow]
Next, the procedure of the distribution process by the distribution system 1 according to the embodiment will be described with reference to FIG. FIG. 15 is a flowchart showing an example of the distribution process according to the embodiment.

As shown in FIG. 15, the acquisition unit 131 of the distribution device 100 acquires the distribution request (step S301). For example, the acquisition unit 131 acquires a content distribution request from the terminal device 10 used by each user.

Then, the estimation unit 132 of the distribution device 100 estimates the user and the context (step S302). For example, the estimation unit 132 estimates the user of the distribution request source from the information for identifying the user acquired from the terminal device 10. Further, for example, the estimation unit 132 estimates the context based on the time when the distribution request is made and the position of the terminal device 10.

After that, the determination unit 134 of the distribution device 100 determines the model (step S303). For example, the determination unit 134 determines a model for determining the content to be delivered to the terminal device 10.

Then, the determination unit 134 determines the order of the contents (step S304). For example, the determination unit 134 calculates the score of the content based on the model and the content determined in step S303. Further, for example, the determination unit 134 determines the ranking of each content based on the calculated score.

After that, the distribution unit 135 of the distribution device 100 distributes the content based on the order (step S305). For example, the distribution unit 135 distributes the content based on the order determined in step S304.

[6. Estimate processing flow]
Next, the procedure of the estimation process by the distribution system 1 according to the embodiment will be described with reference to FIG. FIG. 16 is a flowchart showing an example of the estimation process according to the embodiment.

As shown in FIG. 16, the acquisition unit 131 of the distribution device 100 acquires information about the model (step S401). For example, the acquisition unit 131 acquires information about the model from the cluster information storage unit 124 and the learning information storage unit 125.

Then, the estimation unit 132 of the distribution device 100 calculates the interest score based on the information about the model (step S402). In FIG. 1, the estimation unit 132 calculates the interest score in each context for each user by using the model information MD11. For example, the estimation unit 132 calculates the interest score for each target context for each user, as shown in the interest information storage unit 127.

Then, the estimation unit 132 estimates the user's interest based on the interest score (step S403). In FIG. 1, the estimation unit 132 estimates the interest of the user U3 as shown in the interest information ES11. For example, the estimation unit 132 estimates the interest information ES11-1 indicating the overall interest of the user U3 over the entire context. For example, the estimation unit 132 estimates that the user U3 as a whole is highly interested in the economy. Further, in FIG. 1, the estimation unit 132 estimates interest information ES11-2 indicating interest in each context of the user U3. For example, the estimation unit 132 estimates that the user U3 is highly interested in the economy when he is in the contexts CT12, CT14, CT16, CT18, that is, in the office. Further, for example, the estimation unit 132 estimates that the user U3 is highly interested in sports when the user U3 is at the context CT11, that is, at home (home) in the morning.

[7. Flow of ad delivery processing]
Next, with reference to FIG. 17, a procedure for processing advertisement distribution based on interest information by the advertising device 50 will be described. FIG. 17 is a flowchart showing an example of advertisement distribution processing based on the estimated user interest according to the embodiment.

As shown in FIG. 17, the advertising device 50 acquires interest information from the distribution device 100 (step S501). For example, the advertising device 50 acquires the interest information ES11 from the distribution device 100.

Then, the advertising device 50 determines whether or not the advertising request has been acquired from the terminal device 10 (step S502). If the advertisement request has not been acquired from the terminal device 10 (step S502: No), the advertising device 50 returns to step S502 and repeats the process.

On the other hand, when the advertisement request is acquired from the terminal device 10 (step S502: Yes), the advertising device 50 selects the advertisement based on the interest information (step S503). For example, the advertising device 50 that has acquired the advertisement request from the terminal device 10-3 of the user U3 selects the advertisement to be delivered to the terminal device 10-3 by using the interest information ES11 acquired from the distribution device 100.

After that, the advertising device 50 delivers the selected advertisement (step S504). For example, the advertisement device 50 delivers the advertisement selected based on the interest information ES11 acquired from the distribution device 100 to the terminal device 10-3.

[8. Estimating similar users]
In the above example, the case where the distribution device 100 estimates the user's interest based on the information about the model is shown, but the distribution device 100 estimates the user similar to each user based on the information about the model. May be good. This point will be described with reference to FIGS. 18 and 19. FIG. 18 is a diagram showing an example of model allocation based on similar users according to the embodiment.

In the example of FIG. 18, the distribution device 100 is a user similar to one user based on the allocation of the model for each context in one user, the allocation of the model for each context in another user, and the information about the model. To estimate. Further, the distribution device 100 determines that the model assigned to the context is assigned to the context in which the model is not assigned in one user, and the model assigned to the context is assigned to the user similar to the user.

The cluster information list ML21-1 shown in FIG. 18 shows a state in which the user U21 is added to the cluster information storage unit 124 shown in FIG. For example, it is assumed that the user U21 is a female user in her twenties. Here, based on the similarity of user attributes, the user U21 is similar to the same female user U2 in her twenties. However, the model assigned to each context of user U2 and the model assigned to each context of user U21 have low similarity.

On the other hand, based on the similarity of user attributes, the user U21 has a low similarity to the male user U3 in his thirties. However, the model assigned to each context of user U21 has high model similarity assigned to each context of user U3. Therefore, the distribution device 100 estimates that the user similar to the user U21 is the user U3 (step S31). In this way, the distribution device 100 estimates users similar to each user based on the similarity of the models assigned to each user's context.

Further, in FIG. 18, a model is not assigned to the context CT17 of the user U21. On the other hand, the model M5 is assigned to the context CT17 of the user U3. Therefore, the distribution device 100 assigns the model M5 assigned to the context CT17 of the user U3 similar to the user U21 to the context CT17 of the user U21 whose model is not assigned (step S32). As a result, the distribution device 100 assigns the model M5 to the context CT17 of the user U21 as shown in the cluster information list ML21-2.

Further, the distribution device 100 may estimate a user similar to one user based on the pattern of model allocation for each context in one user and the pattern of model allocation for each context in another user. Good. This point will be described with reference to FIG. FIG. 19 is a diagram showing an example of model allocation based on similar users according to the embodiment.

The cluster information list ML31-1 shown in FIG. 19 shows a state in which the user U31 is added to the cluster information storage unit 124 shown in FIG. For example, it is assumed that the user U21 is a female user in her thirties. Here, based on the similarity of user attributes, the user U21 is similar to the same female user U2 in her twenties and male user U3 in her thirties. However, the model assigned to each context of user U2 and user U3 and the model assigned to each context of user U21 have low similarity.

On the other hand, based on the similarity of user attributes, the user U21 has a low similarity to the male user U4 in his twenties. However, there is a high degree of similarity between the pattern of model allocation for each context in user U21 and the pattern of model allocation for each context in user U4. Specifically, when the model assigned to the context of the user U4 is shifted by one time zone, the model assigned to the context of the user U31 is highly similar. For example, for a model assigned to the context of user U31, if the morning time zone is shifted to noon, the daytime time zone is shifted to night, the night time zone is shifted to midnight, and the midnight time zone is shifted to morning. , High similarity to the model assigned to the context of user U31. That is, the user U31 is assigned a different model when viewed from the user U4 in each context itself, but the model allocation pattern is similar. That is, it is presumed that the user U31 is a user whose life pattern is different from that of the user U4, but the tendency as a whole is similar. Therefore, the distribution device 100 estimates that the user similar to the user U31 is the user U4 (step S33). In this way, the distribution device 100 estimates users similar to each user based on the similarity of the model patterns assigned to each user's context.

Further, in FIG. 19, a model is not assigned to the context CT18 of the user U31. On the other hand, the model M6 is assigned to the context CT16 of the user U4. Therefore, the distribution device 100 assigns the model M6 assigned to the context CT16 of the user U4 similar to the user U31 to the context CT18 of the user U31 whose model is not assigned (step S34). As a result, the distribution device 100 assigns the model M6 to the context CT18 of the user U31 as shown in the cluster information list ML31-2. The user similarity may be estimated based on various tendencies, not limited to the deviation of model allocation as described above. For example, if there is a predetermined similarity between the model allocation pattern of the context corresponding to one user's house and the model allocation pattern of the context corresponding to another user's office, the distribution device 100 is used by one user. May be presumed to be similar to other users. For example, a predetermined model allocation pattern of contexts CT11, CT13, CT15, CT17 corresponding to one user's home and a model allocation pattern of contexts CT12, CT14, CT16, CT18 corresponding to another user's office. If there are similarities, the distribution device 100 may presume that one user is similar to another user.

[9. effect〕
As described above, the distribution device 100 according to the embodiment has an acquisition unit 131 and an estimation unit 132. The acquisition unit 131 is behavior information related to the behavior of each user, and is information in which the division information, which is the behavior information divided based on the context, is combined based on the tendency of the behavior information included in each of the division information. Is generated for each union information, which is information including combinations across users, and acquires information on a model that predicts a predetermined target. The estimation unit 132 estimates information about the user based on the information about the model acquired by the acquisition unit 131.

As a result, the distribution device 100 according to the embodiment also takes into account the behavior information of other users by estimating the information about the user based on the information about the model generated for each union information including the combination across the users. Information about the user can be estimated using the model. Therefore, the distribution device 100 can appropriately estimate information about the user.

Further, in the distribution device 100 according to the embodiment, the estimation unit 132 estimates the interest of one user based on the allocation of the model for each context in one user and the information about the model.

As a result, the distribution device 100 according to the embodiment appropriately estimates information about the user based on the model allocation for each context in the user, that is, the model allocation pattern in the user and the information such as the model weight. be able to.

Further, in the distribution device 100 according to the embodiment, the estimation unit 132 estimates the interest of one user in one context based on the allocation of the model of one context in one user and the information about the model.

Thereby, the distribution device 100 according to the embodiment can appropriately estimate the interest of the user in the context based on the model assigned to the context in the user and the information such as the weight of the model.

Further, in the distribution device 100 according to the embodiment, the estimation unit 132 is based on the allocation of the model for each context in one user, the allocation of the model for each context in another user, and the information about the model. Estimate a user who is similar to the user.

As a result, the distribution device 100 according to the embodiment is based on information such as the allocation of the model for each context in the user, the allocation of the model for each context in the other user, and the weight of the model, and the other user similar to the user. Can be estimated appropriately.

Further, in the distribution device 100 according to the embodiment, the estimation unit 132 is based on the pattern of model allocation for each context in one user and the pattern of model allocation for each context in another user. Estimate users who are similar to.

As a result, the distribution device 100 according to the embodiment appropriately assigns other users similar to the user based on the pattern of model allocation for each context in the user and the pattern of model allocation for each context in other users. Can be estimated to.

Further, the distribution device 100 according to the embodiment has a determination unit 134. The determination unit 134 determines that one user assigns the model assigned to the unassigned context and the user similar to one user assigns the model assigned to the context.

As a result, the distribution device 100 according to the embodiment assigns a model assigned to the context in the context of the user by assigning the model assigned to the context in the context in which the model is not assigned in the user. Can be assigned.

Further, in the distribution device 100 according to the embodiment, when the behavior information about the user identified as one user is collected from the plurality of terminal devices 10, the estimation unit 132 is based on the information about the model. It is estimated whether the user identified as the user is a different user between the user who uses one terminal device 10 and the user who uses the other terminal device 10.

As a result, the distribution device 100 according to the embodiment is identified as one user based on the information about the model when the behavior information about the user identified as one user is collected from the plurality of terminal devices 10. By estimating whether a user is a different user between a user who uses one terminal device 10 and a user who uses another terminal device 10, a plurality of users identified as one user are appropriately separated. can do.

Further, in the distribution device 100 according to the embodiment, the estimation unit 132 adds the behavior information of another user to the behavior information of one user based on the allocation of the model for each context in one user and the information about the model. Estimate if it is included.

As a result, the distribution device 100 according to the embodiment estimates whether the behavior information of one user includes the behavior information of another user based on the allocation of the model for each context in the user and the information about the model. Thereby, a plurality of users identified as one user can be appropriately separated.

Further, in the distribution device 100 according to the embodiment, the estimation unit 132 estimates the information about the user as the information about the model based on the weight of the features included in each model.

As a result, the distribution device 100 according to the embodiment can appropriately estimate the information about the user as the information about the model based on the weight of the features included in each model.

[10. Hardware configuration]
The distribution device 100 according to the above-described embodiment is realized by, for example, a computer 1000 having a configuration as shown in FIG. FIG. 20 is a hardware configuration diagram showing an example of a computer that realizes the function of the distribution device. 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.

The CPU 1100 operates based on a program stored in the ROM 1300 or the HDD 1400, and controls each part. The ROM 1300 stores a boot program executed by the CPU 1100 when the computer 1000 is started, a program that depends on the hardware of the computer 1000, and the like.

The HDD 1400 stores a program executed by the CPU 1100, data used by such a program, and the like. The communication interface 1500 receives data from another device via the network N and sends it to the CPU 1100, and transmits the data generated by the CPU 1100 to the other device via the network N.

The CPU 1100 controls an output device such as a display or a printer, and an input device such as a keyboard or a mouse via the input / output interface 1600. The CPU 1100 acquires data from the input device via the input / output interface 1600. Further, the CPU 1100 outputs the generated data to the output device via the input / output interface 1600.

The media interface 1700 reads a program or data stored in the recording medium 1800 and provides the program or data to the CPU 1100 via the RAM 1200. The CPU 1100 loads the program from the recording medium 1800 onto the RAM 1200 via the media interface 1700, and executes the loaded program. The recording medium 1800 is, for example, an optical recording medium such as a DVD (Digital Versatile Disc) or PD (Phase change rewritable Disk), a magneto-optical recording medium such as MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory. And so on.

For example, when the computer 1000 functions as the distribution device 100 according to the embodiment, the CPU 1100 of the computer 1000 realizes the function of the control unit 130 by executing the program loaded on the RAM 1200. The CPU 1100 of the computer 1000 reads and executes these programs from the recording medium 1800, but as another example, these programs may be acquired from another device via the network N.

Although some of the embodiments and modifications of the present application have been described in detail with reference to the drawings, these are examples and vary based on the knowledge of those skilled in the art, including the embodiments described in the disclosure line of the invention. It is possible to carry out the present invention in another form obtained by modifying or improving the above.

[11. Others]
Further, among the processes described in the above-described embodiments and modifications, all or part of the processes described as being automatically performed can be manually performed, or the processes described as being manually performed. It is also possible to automatically perform all or part of the processed processing by a known method. In addition, the processing procedure, specific name, and information including various data and parameters shown in the above document and drawings can be arbitrarily changed unless otherwise specified. For example, the various information shown in each figure is not limited to the illustrated information.

Further, each component of each of the illustrated devices is a functional concept, and does not necessarily have to be physically configured as shown in the figure. That is, the specific form of distribution / integration of each device is not limited to the one shown in the figure, and all or part of the device is functionally or physically distributed / physically in arbitrary units according to various loads and usage conditions. It can be integrated and configured.

In addition, the above-described embodiments and modifications can be appropriately combined as long as the processing contents do not contradict each other.

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

1 Distribution system 100 Distribution device (estimation device)
121 User information storage unit 122 Behavior information storage unit 123 Divided information storage unit 124 Cluster information storage unit 125 Learning information storage unit 126 Content information storage unit 127 Interest information storage unit 130 Control unit 131 Acquisition unit 132 Estimating unit 133 Generation unit 134 Decision unit 135 Distribution unit 10 Terminal device N network

Claims (9)

The behavior information related to the behavior of each user, which is the behavior information divided based on the context, is the information obtained by combining the division information based on the tendency of the behavior information included in each of the division information, and the user. An acquisition unit that acquires information about a model that predicts a predetermined target, which is generated for each union information that includes combinations that straddle the space.
An estimation unit that estimates information about the user based on the information about the model acquired by the acquisition unit, and an estimation unit.
Equipped with a,
The estimation unit
An estimation device for estimating the interest of a user in each of the plurality of contexts based on the allocation of each model of the plurality of contexts in one user and the information about the model . The estimation unit
A claim characterized in that a user similar to the one user is estimated based on the allocation of the model for each context in one user, the allocation of the model for each context in another user, and the information about the model. Item 1. The estimation device according to item 1. The estimation unit
Claim 2 is characterized in that a user similar to the one user is estimated based on the pattern of model allocation for each context in one user and the pattern of model allocation for each context in another user. The estimation device described in. A decision unit that determines that a model is assigned to an unassigned context in one user and a model assigned to that context in a user similar to the one user.
The estimation device according to any one of claims 1 to 3 , further comprising. The estimation unit
When behavior information about a user identified as one user is collected from a plurality of terminal devices, a user who uses one terminal device by the user identified as the one user based on the information about the model. The estimation device according to any one of claims 1 to 4 , wherein the user is estimated to be a different user from the user who uses the other terminal device. The estimation unit
Claim 1 is characterized in that it estimates whether the behavior information of the one user includes the behavior information of another user based on the allocation of the model for each context in one user and the information about the model. The estimation device according to any one of 5 to 5 . The estimation unit
The estimation device according to any one of claims 1 to 6 , wherein the information about the user is estimated based on the weight of the features included in each model as the information about the model. An estimation method performed by a computer
The behavior information related to the behavior of each user, which is the behavior information divided based on the context, is the information obtained by combining the division information based on the tendency of the behavior information included in each of the division information, and the user. An acquisition process that acquires information about a model that predicts a predetermined target, which is generated for each union information that includes combinations that straddle the interval.
An estimation process that estimates information about the user based on the information about the model acquired by the acquisition process, and an estimation process.
Only including,
The estimation process is
An estimation characterized in that the interest of the one user in each of the plurality of contexts is estimated based on the allocation of each model of the plurality of contexts in one user and the information about the model. Method. The behavior information related to the behavior of each user, which is the behavior information divided based on the context, is the information obtained by combining the division information based on the tendency of the behavior information included in each of the division information, and the user. An acquisition procedure for acquiring information about a model that predicts a predetermined target, which is generated for each union information that includes combinations that straddle the interval.
An estimation procedure for estimating information about the user based on the information about the model acquired by the acquisition procedure, and an estimation procedure.
Let the computer run
The estimation procedure is
An estimation program comprising estimating the interest of the one user in each of the plurality of contexts based on the allocation of each model of the plurality of contexts in one user and the information about the model .

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