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

Abstract

To provide appropriate information to a user.SOLUTION: An information processor has a determination part, and an exclusion part. The determination part determines whether a user context of a distribution destination user as a distribution destination of information as a distribution object is similar to a user context when an opt-out of the distribution object is selected by the distribution destination user. When it is determined by the determination part that the user context of the distribution destination user is similar to the user context when the opt-out of the distribution object is selected, the extrusion part extrudes a model corresponding to a distribution object in which the opt-out out of a plurality of models for determining the distribution destination is selected from a use object for a limited period.SELECTED DRAWING: Figure 5

Description

[Technical Field]

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

[0002] Conventionally, there are known techniques for providing information such as advertisements according to user attributes. For example, Patent Document 1 discloses a technique for extracting advertisements according to user attributes and delivering the extracted advertisements to the user. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 11-024603 Summary of the Invention [Problem to be solved by the invention]

[0004] However, the conventional technology has room for improvement in terms of providing appropriate information to users.

[0005] The present application has been made in view of the above, and aims to provide an information processing device, an information processing method, and an information processing program that can provide appropriate information to a user. [Means for solving the problem]

[0006] The information processing device according to the present application includes a determination unit and an exclusion unit. The determination unit determines whether a user context of a destination user to whom information to be distributed is to be distributed is similar to a user context when the destination user selected to opt out of the distribution target. When the determination unit determines that the user context of the destination user is similar to the user context when the destination user selected to opt out of the distribution target, the exclusion unit excludes, from among a plurality of models determining the distribution destination, a model corresponding to the distribution target for which the opt-out was selected, from targets for use for a limited period of time. [Effects of the Invention]

[0007] According to one aspect of the embodiment, appropriate information can be provided to the user. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram for explaining a process that is a prerequisite for information processing according to the embodiment. [Figure 2] FIG. 2 is a diagram illustrating an example of information processing according to the embodiment. [Figure 3] FIG. 3 is a diagram illustrating an example of the configuration of an information processing system according to the embodiment. [Figure 4] FIG. 4 is a diagram illustrating an example of the configuration of a terminal device according to the embodiment. [Figure 5] FIG. 5 is a diagram illustrating an example of the configuration of an information processing device according to the embodiment. [Figure 6] FIG. 6 is a diagram illustrating an example of attribute information according to the embodiment. [Figure 7] FIG. 7 is a diagram illustrating an example of model information according to the embodiment. [Figure 8] FIG. 8 is a diagram illustrating an example of opt-out user information according to the embodiment. [Figure 9] FIG. 9 is a diagram illustrating an example of history information according to the embodiment. [Figure 10] FIG. 10 is a diagram illustrating an example of user context information according to the embodiment. [Figure 11] FIG. 11 is a flowchart illustrating an example of a processing procedure executed by the information processing device according to the embodiment. [Figure 12] FIG. 12 is a hardware configuration diagram showing an example of a computer that realizes the functions of the information processing device according to the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, modes for implementing an information processing device, an information processing method, and an information processing program according to the present application (hereinafter referred to as "embodiments") will be described in detail with reference to the drawings. Note that the information processing device, the information processing method, and the information processing program according to the present application are not limited to the multiple embodiments described below. Furthermore, the multiple embodiments described below can be appropriately combined as long as the processing content is not contradictory. Furthermore, the same components in the multiple embodiments described below will be assigned the same reference numerals, and redundant explanations will be omitted.

[0010] [1. Overview of information processing] (1-1. Prerequisite processing) An outline of the process that is a prerequisite for the information processing according to the embodiment will be described with reference to Fig. 1. Fig. 1 is a diagram for explaining the process that is a prerequisite for the information processing according to the embodiment.

[0011] As shown in FIG. 1, the information processing apparatus 100 according to the embodiment includes users U1, U2, . . . , U m Terminal devices 21, 22,...,2 used by m In the case shown in FIG. 1, m is an integer equal to or greater than 3. In the following, users U1, U2, . . . , U m When each of the terminal devices 21, 22, . . . , 2 m When each of these is not individually indicated, they may be collectively referred to as the terminal device 2.

[0012] The information processing device 100 receives distribution information D1, D2, D3, D4, D5, . . . , D k is distributed to user U. In the case shown in FIG. 1, k is an integer equal to or greater than 6. Distribution information D1, D2, D3, D4, D5, . . . , D k The distribution to user U is, for example, distribution information D1, D2, D3, D4, D5, . . . , D k This is done by distributing the information D1, D2, D3, D4, D5, . . . , D k When each of these is not individually indicated, they may be collectively referred to as distribution information D.

[0013] The distribution information D is information displayed on the terminal device 2. For example, the distribution information D includes at least one of text information and image information. Image information corresponds to still image information, animation information, video information, etc.

[0014] The information processing device 100 is configured to select a plurality of models M1, M2, M3, M4, . . . , M to determine the destination of distribution information D, which is information to be distributed. n In the case shown in FIG. 1, n is an integer equal to or greater than 5. A plurality of models M1, M2, M3, M4, . . . , M n For example, for objects T1,T2,T3,T4,···,T n This is a model that determines whether or not a user U is interested in each piece of information.

[0015] Model M1 is a model corresponding to target T1. Model M1 determines whether a user U who is interested in the information of target T1 is interested in the distributed information D. Model M2 is a model corresponding to target T2. Model M2 determines whether a user U who is interested in the information of target T2 is interested in the distributed information D. Model M3 is a model corresponding to target T3. Model M3 determines whether a user U who is interested in the information of target T3 is interested in the distributed information D. Model M4 is a model corresponding to target T4. Model M4 determines whether a user U who is interested in the information of target T4 is interested in the distributed information D. Model M n is the target T n This is the model that corresponds to Model M. n is the target T n It is determined whether a user U who has an interest in the information of the target T1, T2, T3, T4, . . . , T n When each of these is not individually distinguished, they may be collectively referred to as the target T. In the following, the models M1, M2, M3, M4, . . . , M n When referring to each of these without distinguishing them individually, they may be collectively referred to as Model M.

[0016] When the information provided by the information processing device 100 is news content, the target T may be, for example, politics, economics, international affairs, business, entertainment, sports, hobbies, or society. The target T may also be a more specific object of any of these. For example, the target T may be a more specific object of hobbies, such as cars, bicycles, motorcycles, movies, music, camping, or travel. The information provided by the information processing device 100 may also be advertising content instead of or in addition to news content, or may be content other than news content and advertising content.

[0017] Model M is a learning model that receives delivery information D as input and outputs a score indicating the degree to which user U is interested in delivery information D. For example, when information on target T1 is input to model M1, the score output from model M1 is higher than when information on target T2 is input to model M1. Also, when information on target T2 is input to model M2, the score output from model M2 is higher than when information on target T1 is input to model M2.

[0018] The model M is generated for each target T by machine learning using a dataset including, for example, information to be distributed and information indicating whether a user U who has an interest in the target T has an interest in the information to be distributed. For example, the model M1 is generated by machine learning using a dataset including the information to be distributed and information indicating whether a user U who has an interest in the target T1 has an interest in the information to be distributed.

[0019] The model M can be generated by machine learning using a neural network such as a convolutional neural network or a recurrent neural network. However, without being limited to such examples, the model M may be generated using machine learning using a learning algorithm such as linear regression or logistic regression instead of a neural network.

[0020] When distributing distribution information D, the information processing device 100 distributes models M1, M2, M3, M4, . . . , M n Input distribution information D to each of the models M1, M2, M3, M4, . . ., M n The distribution information D is distributed to a user U associated with a model whose output score satisfies a preset distribution condition. The preset distribution condition is the distribution of the models M1, M2, M3, M4, . . . , M n The model may be the one with the highest output score or the one with an output score equal to or greater than a preset threshold.

[0021] Here, it is assumed that models M1, M2, M3, M4, and M5 are associated with user U1, and that distribution information D1 is information on target T1, distribution information D2 is information on target T2, distribution information D3 is information on target T3, and distribution information D4 is information on target T4. Furthermore, although not shown in FIG. 1, it is assumed that distribution information D5 is information on target T5.

[0022] In this case, the information processing device 100 uses the models M1, M2, M3, M4, and M5 to determine the user U1 as one of the destinations of the distribution information D1, D2, D3, D4, and D5, and distributes the distribution information D1, D2, D3, D4, and D5 to the user U1 (step S1). In the example shown in FIG. 1, the distribution of the distribution information D1, D2, D3, D4, and D5 to the user U1 is performed by distributing the distribution information D1, D2, D3, D4, and D5 to the terminal device 21.

[0023] When user U1, to whom distribution information D1, D2, D3, D4, and D5 has been distributed, determines that distribution of distribution information D1 is undesirable among the distribution information D1, D2, D3, D4, and D5, user U1 selects opt-out for model M1 (step S2). User U1's selection of opt-out for model M1 indicates user U1's intention to refuse notification of information distributed using model M1, and is performed, for example, by user U1 operating an operation unit (e.g., operation unit 12) provided on terminal device 21. Opting out for model M1 can also be said to be opt-out for target T1 corresponding to model M1.

[0024] When the user U1 selects opt-out for the model M1, opt-out information, which is information indicating that the user U1 has selected opt-out for the model M1, is transmitted from the terminal device 21 to the information processing device 100 (step S3).

[0025] On the other hand, when the information processing device 100 receives the opt-out information from the terminal device 21, it accepts the target T1 corresponding to the model M1 as an opt-out target and excludes and registers the user U1 from the model M1 (step S4). The information processing device 100 excludes and registers the user U1 from the model M1, for example, by canceling the association of the model M1 with the user U1.

[0026] Thereafter, the information processing device 100 starts distributing information of the plurality of distribution targets excluding the target T1 who is the opt-out target to the user U1 (step S5). In step S5, the information processing device 100 stops distributing information using the model M1, thereby distributing information of the plurality of distribution targets excluding the target T1 who is the opt-out target to the user U1.

[0027] Furthermore, when the timing for recommending opt-out cancellation for model M1 arrives, the information processing device 100 generates recommendation information recommending cancellation of opt-out for the opt-out target (step S6).Then, the information processing device 100 delivers the generated recommendation information to user U1 by transmitting it to the terminal device 21 (step S7).

[0028] When the terminal device 21 receives the recommendation information from the information processing device 100, it displays the recommendation information (step S8). The recommendation information displayed on the terminal device 21 includes distribution information D that would be distributed if the opt-out target does not opt-out. In the example shown in FIG. 1, if the opt-out target is entertainment and the opt-out target does not opt-out, information such as "Is actress A getting married?", "Is screenwriter C collapsed from overwork?", and "New drama B will start in September" will be included as recommendation information.

[0029] The recommendation information is information that includes information delivered to user U1 from the information processing device 100 before user U1 selects opt-out of the opt-out target, or information that would be delivered to user U1 from the information processing device 100 if user U1 has not selected opt-out of the opt-out target.

[0030] Therefore, by referring to the recommendation information, user U1 can understand the distribution information D that will be distributed if the opt-out target does not opt ​​out. Therefore, user U1 can appropriately decide whether to cancel the opt-out of the opt-out target. In this way, the information processing device 100 can inform user U of the benefits of canceling the opt-out of the opt-out target, and can provide appropriate information to user U.

[0031] Note that the information to be distributed is not limited to information about the target T using the model M. For example, the information to be distributed may be store information, in which information about each store is the information to be distributed. Even in this case, the information processing device 100 can inform the user U of the benefits of canceling the opt-out of the opt-out target, and can provide the user U with appropriate information.

[0032] (1-2. An example of information processing) An overview of information processing according to an embodiment will be described below with reference to FIG. 2. FIG. 2 is a diagram for explaining an example of information processing according to an embodiment. The information processing according to the embodiment is mainly characterized in that a model corresponding to an opt-out target is automatically opted out based on user context. Note that FIG. 2 describes an example of information processing executed by the information processing device 100 based on the user context of user U1.

[0033] 2, the information processing device 100 has user context information J corresponding to a user U. The user context information J stores, for each target T indicating each distribution target, information indicating the user context when a distribution destination user (for example, user U1) selected opt-out (when opt-out information was accepted), for each user U.

[0034] For example, the information processing device 100 can record user information about the destination user when the destination user selects opt-out, and can adopt a reference vector obtained by vectorizing the recorded user information as user context information. In this case, the information processing device 100 prepares a training dataset in which, for each target T (opt-out target) indicating a delivery target corresponding to the model M for which opt-out has been selected, user information about the destination user when opt-out has been selected by the destination user is associated with label information indicating the opt-out target, and uses the prepared training dataset to train a vector output model that outputs vector information corresponding to the user information.

[0035] For example, when the opt-out target is the same, the information processing device 100 generates a vector output model by optimizing the parameters of the learning model so that a determination target vector obtained by vectorizing user information is similar to a reference vector corresponding to the opt-out target. As a specific example, when the opt-out target is target T1, the parameters of the learning model are optimized so that a determination target vector obtained by vectorizing user information and a reference vector corresponding to target T1 are similar to each other.

[0036] When the information processing device 100 uses an arbitrary network such as a neural network as a learning model, the information processing device 100 performs a learning process to adjust the values ​​of parameters (connection coefficients), which are weights taken into account when scores (values) are propagated between nodes in each layer of the neural network. For example, the information processing device 100 uses an arbitrary method such as backpropagation (error backpropagation) to optimize (correct) the parameters (connection coefficients) so as to reduce the error between the output from the learning model and the correct answer (correct data) corresponding to the input. That is, the information processing device 100 performs a process to optimize the parameters (connection coefficients) so that, when the target T is the same, the cosine similarity between the determination target vector and the reference vector increases, and when the target T is different, the cosine similarity between the determination target vector and the reference vector decreases. When the information processing device 100 uses the backpropagation method, the information processing device 100 can correct the parameters (connection coefficients) so as to reduce the error between the output from the learning model and the correct answer (correct data) corresponding to the input by performing a learning process to minimize a predetermined loss function.

[0037] Furthermore, the information processing device 100 may create a group consisting of multiple users with attributes similar to those of the target user, prepare a learning dataset using information on each user included in the created group, and learn the above-mentioned vector output model. This makes it possible to learn the vector output model even if the amount of information recorded about the target user is not sufficient as learning data.

[0038] The information processing device 100 having the user context information J as described above, for example, at a predetermined timing when determining the destination of the distribution information D, determines whether the user context of the destination user to whom the information to be distributed is to be distributed is similar to the user context when the destination user selected to opt out of the information to be distributed (step S21).

[0039] For example, the information processing device 100 generates a determination target vector obtained by vectorizing user information of a destination user determined as a destination by the model M. Furthermore, the information processing device 100 determines whether or not the determination target vector obtained by vectorizing the user information is similar to a reference vector corresponding to each target T.

[0040] Next, when it is determined that the user context of the distribution destination user is similar to the user context when opt-out of the distribution target was selected, the information processing device 100 excludes, for a limited period of time, the model M corresponding to the distribution target for which opt-out was selected, from the models M that determine the distribution destination of the information to be distributed (step S22). For example, the information processing device 100 sets the target T corresponding to the reference vector determined to be similar to the determination target vector as the opt-out target, and performs exclusion registration of the model M corresponding to the target T. Then, when the model M1 is the exclusion target, the information processing device 100 releases the association of the model M1 that is the exclusion target with the user U1.

[0041] Then, the information processing device 100 starts distributing information of the plurality of distribution targets that reflects the exclusion in step S22 to the distribution destination user (e.g., user U1) determined as the distribution destination by the model M (step S23). For example, the information processing device 100 cancels the association of the model M1 that is the exclusion target with user U1, thereby stopping the distribution of distribution information using the model M1 to user U1. In this way, the information processing device 100 can automatically stop the distribution of information of distribution targets that are likely not desired by the distribution destination user, and can provide appropriate information to user U1.

[0042] Furthermore, the information processing device 100 may exclude the model M from being used for a period designated by the user U. For example, the information processing device 100 may accept a designation of an exclusion period from the user U in advance for each target T indicating a distribution target, and manage the exclusion period in association with the model M corresponding to the target T. Furthermore, the information processing device 100 may exclude the model M from being used for a predetermined period determined in advance for each target T.

[0043] Furthermore, the information processing device 100 may notify the user U of an inquiry as to whether or not to cancel the exclusion of the model M at a predetermined timing, and may cancel the exclusion of the model M in response to a request from the user U. For example, the predetermined timing may be the timing when the exclusion period of the model M has expired.

[0044] Furthermore, after the model M is excluded, the information processing device 100 may determine, at a predetermined timing, whether or not the user context (e.g., the determination target vector) of the destination user (e.g., user U) is similar to the user context (e.g., the reference vector) when the destination user selected to opt out of the distribution target. If the information processing device 100 determines that the user context of the destination user is not similar to the user context when the destination user selected to opt out of the distribution target, it may notify the destination user of an inquiry about whether or not to cancel the exclusion of the model M, and may cancel the exclusion of the model in response to a request from the destination user. This makes it possible to control the device so that, for example, even if the exclusion period for the model M has expired, the cancellation of the opt-out is postponed depending on the user's state.

[0045] [2. Configuration of Information Processing System 1] The configuration of the information processing system according to the embodiment will be described with reference to Fig. 3. Fig. 3 is a diagram showing an example of the configuration of the information processing system according to the embodiment. As shown in Fig. 3, the information processing system 1 according to the embodiment includes terminal devices 21, 22, ..., 2 m and an information processing device 100.

[0046] The terminal device 2 and the information processing device 100 are each connected to a network N. The terminal device 2 can transmit and receive information to and from the information processing device 100 via the network N. The information processing device 100 can transmit and receive information to and from the terminal device 2 via the network N.

[0047] The network N is a communication network such as a LAN (Local Area Network), a WAN (Wide Area Network), a telephone network (such as a mobile phone network or a landline network), a regional IP (Internet Protocol) network, the Internet, etc. The network N may include a wired network or a wireless network.

[0048] The terminal device 2 is a communication terminal used by a user U. A typical example of the terminal device 2 is a smartphone, but it may also be realized by a tablet terminal, a notebook PC (Personal Computer), a desktop PC, a mobile phone, a PDA (Personal Digital Assistant), or the like.

[0049] Furthermore, the terminal device 2 can display, by a web browser or an application, information for using various services provided by the information processing device 100. At this time, when the terminal device 2 receives control information for realizing information display processing by the web browser, the application, or the like from the information processing device 100, the terminal device 2 realizes the display processing in accordance with the received control information.

[0050] The information processing device 100 is an information processing terminal that executes information processing according to the embodiment. A typical example of the information processing device 100 is a server device, but it may also be realized by a mainframe, a workstation, or the like. When the information processing device 100 is realized by a server device, it may be realized by a single server, or may be realized by a cloud system in which multiple server devices and multiple storage devices operate in cooperation with each other.

[0051] Furthermore, the information processing device 100 may function as a distribution device that distributes control information to terminal devices 2 used by users of various services. Here, the control information is written in, for example, a script language such as JavaScript (registered trademark) or a style sheet language such as CSS (Cascading Style Sheets). Note that the application itself distributed from the information processing device 100 may also be considered as control information.

[0052] The various services provided by the information processing device 100 may include API (Application Programming Interface) services corresponding to various applications and various online services. The various online services may include a Q&A service, an Internet connection, a search service, a social networking service (SNS), an electronic commerce service, an electronic payment service, an online game, an online banking service, an online trading service, a hotel reservation service, a ticket reservation service, a video distribution service, a music distribution service, a news distribution service, a map information service, a route search service, a route guidance service, a line information service, an operation information service, a weather information service, and the like.

[0053] The information processing device 100 may also function as a device that distributes advertising content. In this case, the information processing device 100 manages advertising content submitted by advertisers and also manages information related to advertising bids set by the advertisers. For example, the information processing device 100 displays advertising content submitted by advertisers in advertising spaces provided on web pages of various services provided in response to user requests.

[0054] [3. Configuration of Terminal Device 2] An example of the configuration of a terminal device 2 according to an embodiment will be described with reference to Fig. 4. Fig. 4 is a diagram showing an example of the configuration of a terminal device according to an embodiment. As shown in Fig. 4, the terminal device 2 includes a communication unit 10, a display unit 11, an operation unit 12, a sensor group 13, a storage unit 14, and a processing unit 15.

[0055] (Communication Section 10) The communication unit 10 is realized by, for example, a network interface card (NIC). The communication unit 10 is connected to a network N by wire or wirelessly. The communication unit 10 transmits and receives information to and from the information processing device 100 via the network N.

[0056] (Display section 11) The display unit 11 is, for example, an LCD (Liquid Crystal Display) or an organic EL (Electro Luminescence) display.

[0057] (Operation unit 12) The operation unit 12 includes, for example, a keyboard including keys for inputting letters, numbers, and spaces, an enter key, arrow keys, etc., a mouse, a power button, etc. When the display unit 11 is a touch panel display device, the operation unit 12 may be a touch panel.

[0058] (Sensor group 13) The sensor group 13 includes, for example, an acceleration sensor, a gyro sensor, a geomagnetic sensor, an illuminance sensor, and an image sensor. The acceleration sensor is a sensor that detects the acceleration of the terminal device 2. The gyro sensor is a sensor that detects the attitude of the terminal device 2, such as the tilt and rotation. The geomagnetic sensor is a sensor that detects geomagnetism. The illuminance sensor is a sensor that detects illuminance that indicates the brightness of the surroundings of the terminal device 2, and the image sensor is a sensor that captures images of the surroundings of the terminal device 2.

[0059] (Storage unit 14) The storage unit 14 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory, or a storage device such as a hard disk or an optical disk.

[0060] The storage unit 14 stores, for example, information acquired by the processing unit 15 via the network N and the communication unit 10, and detection information that is information detected by the sensor group 13, and the like.

[0061] (Processing section 15) The processing unit 15 is a controller, and is realized, for example, by a CPU (Central Processing Unit) or an MPU (Micro Processing Unit) executing various programs stored in a storage device inside the terminal device 2 using RAM as a working area.

[0062] (Information acquisition unit 16) The information acquisition unit 16 acquires information transmitted from the information processing device 100 and received by the communication unit 10 via the network N. For example, the information transmitted from the information processing device 100 is distribution information D and the like.

[0063] (Display processing unit 17) The display processing unit 17 displays the information acquired by the information acquisition unit 16 on the display unit 11. For example, the display processing unit 17 displays a screen including the distribution information D acquired by the information acquisition unit 16 on the display unit 11.

[0064] (Output unit 18) The output unit 18 transmits, for example, operation information corresponding to an operation on the operation unit 12 by the user U to the information processing device 100 via the communication unit 10.

[0065] For example, when the user U selects opt-out by operating the operation unit 12, the output unit 18 transmits opt-out information to the information processing device 100 via the communication unit 10. The opt-out information includes identification information unique to the distribution target, model M, or distribution information D for which opt-out has been selected.

[0066] Furthermore, the output unit 18 transmits detection information, which is information detected by the sensor group 13, to the information processing device 100 via the communication unit 10.

[0067] [4. Configuration of Information Processing Device 100] An example of the configuration of the information processing device 100 according to the embodiment will be described with reference to Fig. 5. Fig. 5 is a diagram showing an example of the configuration of the information processing device according to the embodiment. As shown in Fig. 5, the information processing device 100 includes a communication unit 110, a storage unit 120, and a control unit 130.

[0068] (Regarding the communication unit 110) The communication unit 110 is realized by, for example, a network interface card (NIC). The communication unit 110 is connected to a network N by wire or wirelessly. The information processing device 100 transmits and receives information to and from the terminal device 10 via the network N.

[0069] (Regarding the storage unit 120) The storage unit 120 is realized by, for example, a semiconductor memory element such as a random access memory (RAM) or a flash memory, or a storage device such as a hard disk or an optical disk. The storage unit 120 has an attribute information storage unit 121, a model information storage unit 122, a distribution information storage unit 123, an opt-out user information storage unit 124, a history information storage unit 125, and a user context information storage unit 126.

[0070] (Attribute information storage unit 121) The attribute information storage unit 121 stores attribute information indicating attributes of the user U. Fig. 6 is a diagram showing an example of attribute information according to the embodiment.

[0071] 6, the attribute information stored in the attribute information storage unit 121 has a plurality of items such as a "user ID" item, an "attribute" item, etc. These items in the attribute information are associated with each other.

[0072] The "User ID" field stores identification information (user ID) for identifying user U. The "Attributes" field stores information indicating user attributes, such as demographic attributes and psychographic attributes. Demographic attributes are demographic attributes of user U, such as gender, age, address, occupation, or annual income. Psychographic attributes are attributes that indicate user U's values, lifestyle, personality, preferences, etc.

[0073] (Model information storage unit 122) The model information storage unit 122 stores model information including information on the model M. Fig. 7 is a diagram showing an example of model information according to the embodiment.

[0074] 7, the model information stored in the model information storage unit 122 has a plurality of items such as a "model ID" item, a "model" item, a "user list" item, etc. These items in the model information are associated with each other.

[0075] In the "model ID" item, identification information (model ID) for identifying a model is stored.

[0076] The "model" field stores information about a model M that determines the distribution destination of the distribution information D. The model M may be an interest model that determines whether the user U is interested in the distribution information D, or a user context model that determines whether the context of the user U is a context defined by the model. For example, the user context model receives detection information transmitted from the terminal device 2 as input and determines whether the context of the user U is a context defined by the model. For example, various user contexts such as "bought a PC (Personal Computer)," "visited store Z," or "cooking food" are assumed as contexts defined by the model. The "model" field may also store information about a model that vectorizes the user context of the user U.

[0077] In the "user list" item, a list of users U associated with the model M is stored.

[0078] (Distribution information storage unit 123) The distribution information storage unit 123 stores distribution information D, which is information distributed to the user U. The distribution information D is web content such as news or information provided to the user U by email, and may include advertising content. When the distribution information D is web content such as news, the distribution information D is displayed on a timeline on the terminal device 2, but the distribution information D is not limited to information displayed on a timeline on the terminal device 2.

[0079] (Opt-out user information storage unit 124) The opt-out user information storage unit 124 stores opt-out user information including the user ID of a user U who has been set to opt out for each model M. Fig. 8 is a diagram illustrating an example of opt-out user information according to the embodiment.

[0080] 8, the opt-out user information stored in the opt-out user information storage unit 124 has multiple items such as a "model ID" item and an "opt-out user list" item. These items in the opt-out user information are associated with each other.

[0081] In the "model ID" item, identification information (model ID) for identifying a model is stored.

[0082] The "opt-out user list" item stores a list of users U who have selected opt-out for model M. The list of users U is made up of the user IDs of each user U who has selected opt-out for model M.

[0083] The opt-out user information storage unit 124 may store information indicating the opt-out period in association with the user ID for which opt-out is set. The opt-out period may be set by the user U, or may be arbitrarily determined by the information processing device 100 for each model M.

[0084] (History information storage unit 125) The history information storage unit 125 stores various types of information related to history information (log data) that indicates the behavior of the user U. Fig. 9 is a diagram showing an example of history information according to an embodiment. In the example shown in Fig. 9, the history information stored in the history information storage unit 125 has multiple items such as a "user ID" item, a "location history" item, a "search history" item, a "browsing history" item, a "purchase history" item, and a "posting history" item. These items are associated with each other.

[0085] Note that FIG. 9 shows an example of history information according to an embodiment, and the history information may include various types of information other than that shown in FIG. 9 depending on the purpose. For example, the history information may include a usage history of a predetermined service by the user U. The history information may also include a store visit history or facility visit history of the user U. The history information may also include a payment history (electronic payment) of the user U using the terminal device 2. Also, FIG. 9 shows an example in which conceptual information is displayed for multiple items in the history information, but in reality, specific information such as a numerical value or a character string corresponding to each item is stored.

[0086] The "user ID" field stores identification information for identifying user U. The "location history" field stores information indicating a location history, which is a history of user U's locations and movements. The "search history" field stores information indicating a search history, which is a history of search queries entered by user U. The "browsing history" field stores information indicating a browsing history, which is a history of content viewed by user U. The "purchase history" field stores information indicating a purchase history, which is a history of purchases made by user U. The "posting history" field stores information indicating a posting history, which is a history of posts made by user U.

[0087] Figure 9 shows that user U, identified by user ID: "UA111," moved as shown in "Location History R1-1," searched as shown in "Search History #1," viewed content as shown in "Browse History #1," purchased specific products at specific stores as shown in "Purchase History #1," and posted as shown in "Post History."

[0088] (User context information storage unit 126) The user context information storage unit 126 stores, for each user U, user context information indicating the user context when the user U selected opt-out for a target T indicating a delivery target. Fig. 10 is a diagram illustrating an example of user context information according to the embodiment.

[0089] 10, the user context information stored in the user context information storage unit 126 has multiple items such as a "user ID" item and a "context information" item. These items in the user context information are associated with each other.

[0090] The "user ID" field stores identification information for identifying the user U. The "context information" field stores, for each target T indicating a distribution target, user context information indicating the user context when the user U selected opt-out. The user context information corresponds to, for example, vector information obtained by vectorizing information about the user when the user U selected opt-out. The information about the user corresponds to, for example, detection information and history information transmitted from the terminal device 2 to the information processing device 100. The information processing device 100 can generate user context information using a model that inputs detection information and history information, which are information about the user, and outputs vectors corresponding to the detection information and history information.

[0091] The user context information storage unit 126 may store information indicating the opt-out period in association with the user ID.

[0092] (Regarding the control unit 130) The control unit 130 is realized by a CPU (Central Processing Unit), an MPU (Micro Processing Unit), or the like executing various programs stored in a storage device inside the information processing device 100 using RAM as a work area. The control unit 130 is also realized by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

[0093] For example, the control unit 130 transmits and receives information to and from the terminal device 2 based on information received by the communication unit 20 and information stored in the storage unit 21. As shown in FIG. 5, the control unit 130 has an acquisition unit 131, a distribution unit 132, a reception unit 133, a recommendation unit 134, a determination unit 135, and an exclusion unit 136, and realizes or executes the functions and actions of the information processing described below. Note that the internal configuration of the control unit 130 is not limited to the configuration shown in FIG. 5, and may have other configurations as long as they perform the information processing described below. Furthermore, the connection relationship between the processing units included in the control unit 130 is not limited to the connection relationship shown in FIG. 5, and may have other connection relationships.

[0094] (Acquisition part 131) When the communication unit 110 receives information transmitted from the terminal device 2 via the network N, the acquisition unit 131 acquires the information transmitted from the terminal device 2 from the communication unit 110. For example, the information transmitted from the terminal device 2 includes operation information, which is information corresponding to an operation on the operation unit 12 by the user U, and detection information, which is information detected by the sensor group 13. Furthermore, for example, the operation information includes opt-out information and the like.

[0095] (Distribution Section 132) The distribution unit 132 distributes the distribution information D to the user U by transmitting the distribution information D to the terminal device 2 via the communication unit 20 and the network N using multiple models M that determine the distribution destination of the information to be distributed.

[0096] Furthermore, when the model M is an interest model, the distribution unit 132 inputs the distribution information D to a plurality of models M, determines a user U associated with a model M that satisfies a predetermined distribution condition among the plurality of models M as a distribution destination of the distribution information D, and distributes the distribution information D to the determined distribution destination user U. An example of the predetermined distribution condition is, but is not limited to, a condition that the distribution information D is input and the highest score is output, and that the score is equal to or greater than a predetermined threshold value.

[0097] Furthermore, when the model M is a user context model, the distribution unit 132 inputs the detection information acquired by the acquisition unit 131 to each user context model and determines a user context model that satisfies a preset distribution condition from among the scores output by each user context model. An example of the preset distribution condition is, but is not limited to, a condition that the score output from the user context model is equal to or greater than a preset threshold. Furthermore, each user context model is associated with distribution information D. The distribution unit 132 distributes the distribution information D associated with the user context model that outputs a score equal to or greater than a preset threshold to the user U who is the sender of the detection information.

[0098] Furthermore, when the exclusion unit 136 excludes a model M, the distribution unit 132 determines a user U to be a distribution destination using the model M that remains after the exclusion is reflected.

[0099] (Reception Department 133) The reception unit 133 receives the delivery target for which the user U has selected opt-out as the opt-out target. The opt-out target is a target T indicating the delivery target. The reception unit 133 acquires the opt-out information received by the communication unit 110 from the communication unit 110 via the network N as information on the opt-out target.

[0100] Furthermore, the reception unit 133 updates the model information stored in the model information storage unit 122 or the opt-out user information stored in the opt-out user information storage unit 124, based on the opt-out information acquired from the communication unit 110. The opt-out information is opt-out information for model M.

[0101] For example, when the reception unit 133 receives opt-out information for model M1 from user U1, it changes the state in which the user ID of user U1 is associated with model M1 to a state in which it is not associated with model M1 in the model information storage unit 122. Furthermore, when the reception unit 133 receives opt-out information for model M1 from user U1, it associates the user ID of user U1 with the target model for model M1 in the opt-out user information storage unit 124.

[0102] Furthermore, the reception unit 133 acquires an opt-out cancellation request received by the communication unit 110 from the communication unit 110 via the network N. For example, the reception unit 133 updates the model information stored in the model information storage unit 122 or the opt-out user information stored in the opt-out user information storage unit 124.

[0103] (Recommendation Section 134) The recommendation unit 134 notifies the opt-out user of recommendation information recommending cancellation of opt-out for the opt-out target after the distribution unit 132 starts distribution of information of multiple distribution targets excluding the opt-out target to the opt-out user. The opt-out target is a distribution target for which opt-out has been selected by the opt-out user, for example, target T.

[0104] The recommendation information is, for example, information that notifies the opt-out user of the benefits of canceling the opt-out of the opt-out target. This can encourage the opt-out user to cancel the opt-out. The recommendation information also includes information that is delivered to the opt-out user from the delivery unit 132 when the opt-out user does not select opt-out of the opt-out target. This allows the opt-out user to easily understand the benefits of canceling the opt-out of the opt-out target.

[0105] (Judgment unit 135) The determination unit 135 determines whether or not the user context of a delivery destination user who is the delivery destination of the information to be delivered is similar to the user context when the delivery destination user selected opt-out of the delivery target. For example, the determination unit 135 determines whether or not the user context of the delivery destination user is similar to the user context when the delivery destination user selected opt-out of the delivery target (target T indicating the delivery target) at a predetermined timing when determining the delivery destination of the delivery information D.

[0106] Specifically, the determination unit 135 acquires user context information (reference vector) of each target T corresponding to the destination user from the user context information storage unit 126. Then, the determination unit 135 determines whether or not a determination target vector obtained by vectorizing user information of the destination user determined as the destination by the model M is similar to the reference vector corresponding to each target T. The determination unit 135 passes the determination result to the exclusion unit 136.

[0107] (exclusion part 136) When the determination unit 135 determines that the user context of the distribution destination user is similar to the user context when opt-out of the distribution target is selected, the exclusion unit 136 excludes from the targets of use a model corresponding to (a target T indicating) the distribution target for which opt-out has been selected, from among a plurality of models that determine the distribution destination of the information to be distributed. For example, the exclusion unit 136 sets the target T corresponding to the reference vector determined to be similar to the determination target vector as the opt-out target, and performs exclusion registration of the model M corresponding to the target T.

[0108] Specifically, for example, for user U1, if target T1 is an opt-out target, the exclusion unit 136 deletes the user ID of user U1 from the user list associated with model M1 corresponding to target T1 in the model information stored in the model information storage unit 122, and cancels the association between user U1 and model M1 corresponding to target T1. In addition, the exclusion unit 136 adds the user ID of user U1 to the opt-out user list associated with model M1 corresponding to target T1 in the model information stored in the model information storage unit 122, and updates the opt-out user list.

[0109] Furthermore, the exclusion unit 136 can exclude the model M from being used for a period designated by the user U. For example, the exclusion unit 136 receives in advance from the user U a designation of an exclusion period for each target T indicating a distribution target, and manages the period in association with the model M corresponding to the target T.

[0110] Furthermore, the exclusion unit 136 may notify the user U of an inquiry as to whether or not to cancel the exclusion of the model M at a predetermined timing, and may cancel the exclusion of the model M in response to a request from the user U. For example, when the exclusion unit 136 receives a request from the user U1 to cancel the opt-out of the target T1, the exclusion unit 136 registers the user ID of the user U1 in the user list associated with the model M1 corresponding to the target T1 in the model information stored in the model information storage unit 122, and associates the model M1 corresponding to the target T1 with the user U1. Furthermore, the exclusion unit 136 deletes the user ID of the user U1 from the opt-out user list associated with the model M1 corresponding to the target T1 in the model information stored in the model information storage unit 122, and updates the opt-out user list. Note that an example of the predetermined timing is the timing when the exclusion period for the model M expires.

[0111] Furthermore, after model M is excluded, the exclusion unit 136 may determine at a predetermined timing whether or not the user context (for example, the determination target vector) is similar to the user context (for example, the reference vector) when user U selected opt-out. If the exclusion unit 136 determines that the user context is not similar to the user context when user U selected opt-out, it may notify user U of an inquiry about whether or not to cancel the exclusion of model M. This allows, for example, control to postpone cancellation of opt-out depending on the user's state even if the exclusion period for model M has expired.

[0112] [5. Processing Procedure] An example of a processing procedure executed by the information processing device 100 according to the embodiment will be described below with reference to Fig. 11. Fig. 11 is a flowchart showing an example of a processing procedure executed by the information processing device according to the embodiment. The processing procedure shown in Fig. 11 is executed by the control unit 130 of the information processing device 100. The processing procedure shown in Fig. 11 is repeatedly executed while the information processing device 100 is operating.

[0113] 11, the determination unit 135 executes a determination process (step S101). Specifically, the determination unit 135 determines whether or not the user context of the destination user is similar to the user context when opt-out was selected.

[0114] Furthermore, the exclusion unit 136 executes exclusion processing (step S102). Specifically, when it is determined in step S101 that the user context of the delivery destination user is similar to the user context when the delivery destination user selected opt-out, the exclusion unit 136 excludes, from among a plurality of models that determine the delivery destination, a model corresponding to the delivery target for which opt-out was selected, from the target for use for a limited period of time.

[0115] Furthermore, the distribution unit 132 starts information distribution (step S103) and ends the processing procedure shown in Fig. 11. Specifically, the distribution unit 132 starts information distribution for multiple distribution targets that reflects the result of the exclusion process in step S102.

[0116] It should be noted that, when a plurality of recipient users are determined, the processing procedure shown in FIG. 11 is executed for each recipient user.

[0117] [6. Modifications] The information processing device 100 according to the above embodiment may be implemented in various different forms other than the above embodiment, so other embodiments of the information processing device 100 will be described below.

[0118] In the above embodiment, an example has been described in which the information processing device 100 excludes a model corresponding to a distribution target for which opt-out has been selected from among multiple models that determine the distribution destination of information for a distribution target for which opt-out has been selected. However, information processing different from this example may be performed. For example, when performing information distribution corresponding to an opt-out target, the information processing device 100 may receive from the user U a selection of a distribution method for which distribution is to be stopped from multiple distribution methods such as web content, email, and push notification. Furthermore, the information processing device 100 may record information about a distribution method for which distribution has been stopped for each user U, estimate a delivery method that is likely to be selected by the user U, and automatically select a delivery method for which distribution is to be stopped based on the estimation result.

[0119] In the above embodiment, the administrator of the information processing device 100 may analyze the history information stored in the history information storage unit 125 to estimate the user context when the destination user selected to opt out of the delivery target. The administrator then associates the estimated user context with a model corresponding to the delivery target and manages it for each user U. When delivering information to the destination user, the information processing device 100 may determine whether to opt out of the delivery target based on whether the destination user's user context associated with the model corresponding to the delivery target is similar to the destination user's current user context. This allows the information processing device 100 to implement specific individual responses, such as estimating the destination user's interests from the destination user's search history and excluding a model that determines a destination for information that the destination user is unlikely to be interested in, excluding a model that determines a destination for information corresponding to a predetermined area when the destination user's location is within a predetermined area, excluding a model that determines a destination for food-related information when the destination user's weight exceeds a threshold, or excluding a model that delivers bargain-related information when the destination user's card usage amount exceeds a predetermined amount.

[0120] [7. Hardware Configuration] The information processing device 100 according to the embodiment described above is realized, for example, by a computer 1000 having a configuration as shown in Fig. 12. Fig. 12 is a hardware configuration diagram showing an example of a computer that realizes the functions of the information processing device according to the embodiment.

[0121] The computer 1000 is connected to an output device 1010 and an input device 1020, and has a configuration in which an arithmetic unit 1030, a primary storage device 1040, a secondary storage device 1050, an output IF (Interface) 1060, an input IF 1070, and a network IF 1080 are connected by a bus 1090.

[0122] The arithmetic device 1030 operates based on programs stored in the primary storage device 1040 and secondary storage device 1050, programs read from the input device 1020, and the like, and executes various processes. The primary storage device 1040 is a memory device, such as a RAM, that temporarily stores data used by the arithmetic device 1030 for various calculations. The secondary storage device 1050 is a storage device in which data used by the arithmetic device 1030 for various calculations and various databases are registered, and is realized by a ROM (Read Only Memory), HDD, flash memory, or the like.

[0123] The output IF 1060 is an interface for transmitting information to be output to an output device 1010 that outputs various types of information, such as a monitor or a printer, and is realized by a connector conforming to a standard such as USB (Universal Serial Bus), DVI (Digital Visual Interface), or HDMI (High Definition Multimedia Interface), etc. The input IF 1070 is an interface for receiving information from various input devices 1020, such as a mouse, keyboard, and scanner, and is realized by a USB, for example.

[0124] The input device 1020 may be a device that reads information from an optical recording medium such as a CD (Compact Disc), a DVD (Digital Versatile Disc), or a PD (Phase Change Rewritable Disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), a tape medium, a magnetic recording medium, or a semiconductor memory. The input device 1020 may also be an external storage medium such as a USB memory.

[0125] The network IF 1080 receives data from other devices via the network N and sends it to the arithmetic device 1030, and also transmits data generated by the arithmetic device 1030 to other devices via the network N.

[0126] The arithmetic unit 1030 controls the output device 1010 and the input device 1020 via the output IF 1060 and the input IF 1070. For example, the arithmetic unit 1030 loads a program from the input device 1020 or the secondary storage device 1050 onto the primary storage device 1040 and executes the loaded program.

[0127] For example, when the computer 1000 functions as the information processing device 100 according to the above embodiment, the arithmetic device 1030 of the computer 1000 executes a program (for example, an information processing program) loaded onto the primary storage device 1040, thereby realizing the same functions as the control unit 130. That is, the arithmetic device 1030 cooperates with the program (for example, an information processing program) loaded onto the primary storage device 1040 to realize the processing by the information processing device 100 according to the above embodiment.

[0128] [8. Other] Of the processes described in the above embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically using a known method.In addition, the information including the processing procedures, specific names, various data and parameters shown in the above documents and drawings can be changed as desired unless otherwise specified.

[0129] Furthermore, the components of each device shown in the figure are conceptual functional components and do not necessarily have to be physically configured as shown in the figure. In other words, the specific form of distribution and integration of each device is not limited to that shown in the figure, and all or part of them can be functionally or physically distributed and integrated in any unit depending on various loads, usage conditions, etc.

[0130] Furthermore, the above-described embodiments can be combined as appropriate within the scope of not causing any contradiction in the processing content.

[0131] [9. Effects] The information processing device 100 according to the embodiment includes a determination unit 135 and an exclusion unit 136. The determination unit 135 determines whether the user context of a delivery destination user to whom information to be delivered is to be delivered is similar to the user context when the delivery destination user selected to opt out of the delivery target. When the determination unit determines that the user context of the delivery destination user is similar to the user context when the delivery target user selected to opt out of the delivery target, the exclusion unit 136 excludes, from among multiple models determining the delivery destination, a model corresponding to the delivery target for which opt-out was selected, from the models to be used for a limited period of time.

[0132] For this reason, the information processing device 100 according to the embodiment can automatically stop the distribution of information that is likely to be distributed to a destination user and that the destination user does not want to receive, and can provide appropriate information to the user.

[0133] Furthermore, in the information processing device 100 according to the embodiment, the exclusion unit 136 excludes the model from use for a period designated by the destination user. In this way, the information processing device 100 according to the embodiment can realize a time-limited opt-out that reflects the user's intention, by the processes executed by the above-described units or any combination of the units.

[0134] Furthermore, in the information processing device 100 according to the embodiment, the exclusion unit 136 notifies the destination user at a predetermined timing of an inquiry as to whether or not to cancel the exclusion of the model, and cancels the exclusion of the model in response to a request from the destination user.

[0135] In this way, the information processing device 100 according to the embodiment can re-distribute information that has been opted out of distribution by the processes executed by the above-mentioned units or any combination of the units, thereby providing appropriate information to the user.

[0136] Furthermore, in the information processing device 100 according to the embodiment, after a model is excluded, the determination unit 135 determines, at a predetermined timing, whether or not the user context of the destination user is similar to the user context when the destination user selected to opt out of the distribution target. When the determination unit 135 determines that the user context of the destination user is not similar to the user context when the destination user selected to opt out of the distribution target, the exclusion unit 136 notifies the destination user of an inquiry about whether or not to cancel the exclusion of the model, and cancels the exclusion of the model in response to a request from the destination user.

[0137] In this way, the information processing device 100 according to the embodiment can, through the processing executed by each of the above-mentioned units or any combination of the units, control the cancellation of the opt-out depending on the user's status even if the exclusion period for model M has expired, and can provide the user with appropriate information.

[0138] The above describes in detail the embodiments of the present application based on several drawings, but these are merely examples, and the present invention can be implemented in other forms that include the embodiments described in the Disclosure of the Invention section and that have been modified and improved in various ways based on the knowledge of those skilled in the art.

[0139] Furthermore, the above-mentioned "section, module, unit" can be read as "means" or "circuit," etc. For example, a control section can be read as control means or a control circuit. [Explanation of symbols]

[0140] 1. Information Processing Systems 2. Terminal Device 10. Communications Department 11 Display section 12 Control section 13 Sensors 14 Storage section 15 Processing section 16 Information acquisition department 17 Display processing section 18 Output section 100 Information processing device 110 Communications Department 120 Storage section 121 Attribute information storage unit 122 Model information storage unit 123 Distribution information storage unit 124 Opt-out user information storage unit 125 History information storage unit 126 User context information storage unit 130 Control Unit 131 Acquisition Department 132 Distribution Department 133 Reception Department 134 Recommendation Department 135 Judgment section 136 Exclusion part

Claims

1. A storage unit that stores, for each target corresponding to each of a plurality of models that determine the destination of distribution information, a reference vector that is obtained by vectorizing search information and history information, which are user information of the user when opting out of the model that determines the destination of distribution information, in association with identification information that identifies users who may be destination users of distribution information, which is information to be distributed; a determination unit that acquires from the storage unit the reference vector corresponding to the user determined as the destination user, and determines whether the acquired reference vector is similar to a determination target vector obtained by vectorizing the user information of the destination user; an exclusion unit that, when the determination unit determines that the reference vector and the determination target vector are similar, excludes, from among the plurality of models, the model corresponding to the target determined to have the reference vector and the determination target vector similar, from targets for use for a period designated by the user; An information processing device comprising:

2. The exclusion section is When the period during which the model is excluded from use expires, the destination user is notified of an inquiry as to whether or not to cancel the exclusion of the model, and the exclusion of the model is canceled in response to a request from the destination user.

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

3. The determination unit After the model is excluded, it is determined whether the target vector of the destination user is similar to the reference vector; The exclusion section is If the determination unit determines that the target vector is not similar to the reference vector, the destination user is notified of an inquiry as to whether or not to cancel the exclusion of the model, and the exclusion of the model is canceled in response to a request from the destination user.

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

4. A computer-implemented information processing method, comprising: a determination step of acquiring, from a storage unit that stores, for each target corresponding to each of a plurality of models that determine the destination of the distribution information, a reference vector obtained by vectorizing search information and history information that are user information of the user when opt-out for the model that determines the destination of the distribution information, the reference vector corresponding to the user determined as the destination user, in association with identification information for identifying users who can be destination users of distribution information that is information to be distributed, the reference vector, and determining whether the acquired reference vector is similar to a determination target vector obtained by vectorizing the user information of the destination user; an exclusion step of excluding, from the plurality of models, the model corresponding to the target determined to have a similar reference vector and target vector in the determination step, for a period designated by the user, when the determination step determines that the reference vector and target vector are similar; An information processing method comprising:

5. On the computer, a determination step of acquiring, from a storage unit that stores, for each target corresponding to each of a plurality of models that determine the destination of the distribution information, a reference vector obtained by vectorizing search information and history information that are user information of the user when opt-out for the model that determines the destination of the distribution information, the reference vector corresponding to the user determined as the destination user, in association with identification information for identifying users who may be destination users of distribution information that is information to be distributed, the reference vector corresponding to the user determined as the destination user, and determining whether the acquired reference vector is similar to a determination target vector obtained by vectorizing the user information of the destination user; an exclusion step of excluding, from the plurality of models, the model corresponding to the target determined to have a similar reference vector and target vector to be determined by the determination step, from targets for use for a period designated by the user; An information processing program characterized by causing the program to execute the above.

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