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

Abstract

To provide an information processing device capable of providing better information, an information processing method, and an information processing program.SOLUTION: An information processing device comprises an identification unit, a generation unit, and a provision unit. The identification unit identifies a plurality of pieces of related information related to an action intentionally taken by a user for an object on a Web. The generation unit generates a user vector on the object on the basis of a partial vector generated by individually convolving each of the plurality of pieces of related information in deep learning. The provision unit provides information based on the generated user vector.SELECTED DRAWING: Figure 3

Description

Technical Field

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

Background Art

[0002] Conventionally, various metrics have been used in advertising distribution via the Internet. For example, techniques for predicting various metrics such as CTR (Click Through Rate) and CVR (Conversion Rate), and selecting users to be the advertising distribution destinations based on the prediction results are known (see, for example, Patent Document 1).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] However, in the prior art, there is room for improvement in providing better information based on highly accurate metrics.

[0005] The present application has been made in view of the above, and an object thereof is to provide an information processing apparatus, an information processing method, and an information processing program that can provide better information.

Means for Solving the Problems

[0006] The information processing device according to the present invention comprises a specification unit, a generation unit, and a provision unit. The specification unit identifies a plurality of related pieces of information relating to an action intentionally performed by a user on a target on the web. The generation unit generates a user vector relating to the target based on subvectors generated by individually convolving the plurality of related pieces of information in deep learning. The provision unit provides information based on the generated user vector. [Effects of the Invention]

[0007] According to one embodiment, it has the effect of being able to provide better information. [Brief explanation of the drawing]

[0008] [Figure 1A] Figure 1A is a diagram showing the process performed by the information processing device according to the embodiment. [Figure 1B] Figure 1B shows the process performed by the information processing device according to the embodiment. [Figure 2] Figure 2 shows an example of the configuration of an information processing system according to the embodiment. [Figure 3] Figure 3 shows an example of the configuration of an information processing device according to the embodiment. [Figure 4] Figure 4 shows an example of user information. [Figure 5] Figure 5 shows an example of advertising information. [Figure 6] Figure 6 shows an example of advertiser information. [Figure 7] Figure 7 is a flowchart showing the processing procedure of the information processing device executed by the information processing device according to the embodiment. [Figure 8] Figure 8 shows an example of a hardware configuration. [Modes for carrying out the invention]

[0009] The following describes in detail, with reference to the drawings, embodiments for implementing the information processing device, information processing method, and information processing program according to the present application (hereinafter referred to as "embodiments"). Note that these embodiments do not limit the information processing device, information processing method, and information processing program according to the present application. Furthermore, the same parts are denoted by the same reference numerals in each of the following embodiments, and redundant descriptions are omitted.

[0010] (Embodiment) First, the processes performed by the information processing device according to the embodiment will be described using Figures 1A and 1B. Figures 1A and 1B are diagrams showing the processes performed by the information processing device according to the embodiment. Figure 1A shows an example of the operation of the information processing system S, which includes the information processing device 1 according to the embodiment. Below, we will show a case where the information processing system S targets advertising content (also simply called "advertisements") and performs various information processing related to those advertisements. Note that the target is not limited to advertisements, but can be any content to which the processing described below can be applied.

[0011] As shown in Figure 1A, the information processing system S according to this embodiment includes an information processing device 1, a distribution server 50, a user terminal 100, and an advertiser terminal 200.

[0012] In Figure 1A, the information processing system S according to the embodiment identifies multiple pieces of related information concerning actions intentionally performed by the user on a target (advertisement) on the web. In deep learning, it generates a user vector related to the target based on subvectors generated by individually convolving each of the multiple pieces of related information, and provides information based on the generated user vector.

[0013] Specifically, first, the advertiser terminal 200 performs a process to request the delivery of the advertiser's advertisement (step S1). For example, the advertiser terminal 200 requests the delivery server 20 to deliver the advertisement in response to the advertiser's operation, and the delivery server 50 adds the advertiser's advertisement to the target of delivery to the user terminal 100 in response to the request from the advertiser terminal 200.

[0014] Subsequently, when the user operates the user terminal 100 to use the content distribution service provided by the distribution server 50, the user terminal 100 transmits an advertisement distribution request according to the user's operation (step S2). For example, when the user terminal 100 uses the search service provided by the distribution server 50, if the user clicks on the advertisement content displayed on the search screen (query input screen or search result screen), the user terminal 100 transmits an advertisement distribution request for the clicked advertisement.

[0015] Subsequently, the distribution server 50 distributes the requested advertisement to the user terminal 100 in response to the distribution request from the user terminal 100 (step S3). In addition, when distributing the advertisement, the distribution server 50 acquires various information and provides it to the information processing apparatus 1 (step S4). Details of the information provided to the information processing apparatus 1 will be described later with reference to FIG. 1B.

[0016] Subsequently, the information processing apparatus 1 generates a user vector related to the target based on the information acquired from the distribution server 50 (step S5). The user vector is, for example, a vector indicating the presence or absence of interest in the target advertisement and the degree of interest. That is, the user vector is a vector indicating what advertisements (products or services being advertised) the user is interested in.

[0017] First, the information processing apparatus 1 identifies a plurality of pieces of related information regarding actions intentionally performed by the user on the advertisements on the web. Specifically, the information processing apparatus 1 determines whether a click operation for selecting the displayed advertisement is an intentionally performed action. For example, when the information processing apparatus 1 clicks on the central position of the area where the advertisement is displayed, it identifies such a click operation as an intentional action. Also, when a click operation is performed after a certain period of time has elapsed while the distributed advertisement is being viewed (displayed), the information processing apparatus 1 identifies such a click operation as an intentional action. Further, when a click operation is performed immediately (within a predetermined period) after the advertisement is distributed (displayed), the information processing apparatus 1 identifies such a click operation as not being an intentional action (accidentally selecting the advertisement). Then, when the information processing apparatus 1 determines that it is an intentionally performed action, it identifies the related information related to such an action. The related information includes, for example, the identification information of the site associated with the advertisement selected by the user, the input information of the user when the advertisement is selected (search keyword in the case of a search service), the audience category (classification information classified by interest, concern, attribute, life event from user information), the category of the advertisement selected by the user, and information such as the title (product name, catch copy, etc.).

[0018] Subsequently, the information processing apparatus 1 generates the above user vector based on the identified plurality of pieces of related information. Here, the method of generating the user vector will be described in detail using FIG. 1B. FIG. 1B shows a model of a convolutional neural network (CNN), which is an example of deep learning.

[0019] As shown in FIG. 1B, the information processing apparatus 1 generates a user vector using a deep learning model. Specifically, the information processing apparatus 1 generates a user vector by aggregating a partial vector generated based on a plurality of pieces of related information input to the input layer and a stationary vector generated based on a plurality of pieces of stationary information input to the input layer.

[0020] First, let's explain how to generate a subvector. A subvector is generated by inputting multiple pieces of related information into the input layer. Figure 1B shows an example where the input layer is populated with related information such as audience category (ac_list), search keyword (search_kw), ad category (ad_categories), ad title (ad_title), and ad site ID (site_id). Note that the related information shown in Figure 1B is just an example; any information that changes in response to the actions described above (click operations) can be applied.

[0021] The information processing device 1 generates subvectors in a CNN by convolving multiple related pieces of information. Specifically, the information processing device 1 generates subvectors (vec) by aggregating the data from the intermediate layers (dence_3, density_5, density_7, density_9, density_11) generated by individually convolving multiple related pieces of information. It is preferable that the subvectors are represented as non-negative sparse vectors. This prevents the values ​​of the data in the intermediate layers corresponding to each piece of related information from canceling out due to aggregation.

[0022] Furthermore, while Figure 1B shows an example of generating a subvector based on a click operation for a single advertisement, the information processing device 1 may, for example, generate a subvector for each action for multiple different advertisements and then generate a subvector by aggregating the subvectors for each action.

[0023] Next, we will explain how to generate a stationary vector. A stationary vector is generated by inputting multiple pieces of stationary information into the input layer. Stationary information is constant information about the user or advertisement. Figure 1B shows an example in which the input layer is populated with DS resource record (ds), gender, advertisement format (media_adformat), internet network connection (network), OS (Operating System) type (os_type), advertisement style (adstyle), age, user's location area (area), and user terminal 100 device type (device). Note that the stationary information shown in Figure 1B is just an example, and any information that is constant information about the user or advertisement can be applied.

[0024] The information processing device 1 generates a stationary vector by convolving multiple stationary information. Specifically, the information processing device 1 generates a stationary vector by convolving multiple stationary information into a single value in the intermediate layer.

[0025] The information processing device 1 then generates a user vector based on the generated stationary vector and the partial vector. Specifically, the information processing device 1 generates the user vector, which is the value of the output layer, by aggregating the stationary vector and the partial vector.

[0026] Returning to Figure 1A, as shown in Figure 1A, the information processing device 1 provides information based on the generated user vector to the distribution server 50 and the advertiser terminal 200 (step S6). For example, the distribution server 50 selects advertisements to deliver to the user (display on the search screen) based on the acquired user vector. The information processing device 1 may also estimate, for example, whether the user will intentionally select an advertisement by clicking based on the user vector, and provide the estimation result (the probability of clicking or whether or not a click will be performed) to the distribution server 50.

[0027] Thus, in the embodiment described above, by generating a user vector based on a subvector generated by individually convolving multiple pieces of related information, each piece of related information is less likely to get mixed up and buried in the intermediate layers of deep learning. As a result, a user vector (subvector) that better reflects the features of each piece of related information can be generated. In other words, according to the information processing device 1 of the embodiment, better information can be provided using a highly accurate index (user vector).

[0028] Next, an example of the configuration of the information processing system S according to the embodiment will be described using Figure 2. Figure 2 is a block diagram showing an example of the configuration of the information processing system S according to the embodiment. As shown in Figure 2, the information processing system S according to the embodiment consists of an information processing device 1, a distribution server 50, a user terminal 100, and an advertiser terminal 200, all connected to a network N by wired or wireless means. The network N is, for example, a network such as the Internet, a WAN (Wide Area Network), or a LAN (Local Area Network).

[0029] Information Processing Device 1 is a server device that executes an information processing method according to the embodiment. Information Processing Device 1 identifies multiple pieces of related information relating to an action intentionally taken by a user towards an object (advertisement) on the web, generates a user vector about the object based on a subvector generated by individually convolving each of the multiple pieces of related information in deep learning, and provides information based on the generated user vector.

[0030] Furthermore, the information processing device 1 is an information processing device that works in conjunction with the distribution server 50, the user terminal 100, and the advertiser terminal 200, and provides various API (Application Programming Interface) services for various applications (hereinafter referred to as "apps") and various data to the distribution server 50, the user terminal 100, and the advertiser terminal 200, and is realized by a server device or a cloud system.

[0031] Furthermore, the information processing device 1 may be an information processing device that provides some kind of web service online to the distribution server 50, the user terminal 100, and the advertiser terminal 200. For example, the information processing device 1 may provide services such as internet connection, search services, SNS (Social Networking Service), e-commerce (EC), electronic payment, online games, online banking, online trading, accommodation / ticket reservations, video / music distribution, news, maps, route search, route guidance, route information, service information, and weather forecasts as web services. In practice, the information processing device 1 may cooperate with various servers that provide the above-mentioned web services and act as an intermediary for web services, or it may be responsible for processing web services.

[0032] The distribution server 50 is a server device that distributes various types of content, such as advertisements, to users. The distribution server 50 receives requests for content distribution from clients (advertisers and service providers) and distributes the requested content to users.

[0033] The user terminal 100 is a terminal device owned by the user. The user terminal 100 can be any type of terminal device, such as a smartphone, desktop PC, notebook PC, or tablet PC. The user terminal 100 transmits various information to the information processing device 1 and the distribution server 50, and receives information provided by the information processing device 1 and the distribution server 50, etc.

[0034] The advertiser terminal 200 is a terminal device used by the advertiser who requested the delivery of the advertisement. For example, the advertiser terminal 200 can be any type of terminal device, such as a smartphone, desktop PC, notebook PC, or tablet PC. The advertiser terminal 200 also submits advertisements to the delivery server 50 or other advertisement delivery device (advertising delivery device) according to the advertiser's instructions.

[0035] Next, with reference to Figure 3, an example configuration of the information processing device 1 will be described.

[0036] Figure 3 is a diagram showing an example configuration of an information processing device 1 according to an embodiment. As shown in Figure 3, the information processing device 1 has a communication unit 2, a control unit 3, and a storage unit 4. The control unit 3 includes an acquisition unit 31, a specification unit 32, a generation unit 33, and a provision unit 34. The storage unit 4 stores user information 41, advertising information 42, and advertiser information 43.

[0037] The communication unit 2 is implemented, for example, by a NIC (Network Interface Card). The communication unit 2 is connected to the network by wire or wireless connection.

[0038] The control unit 3 is a controller and is implemented by a processor such as a CPU (Central Processing Unit) or MPU (Micro Processing Unit) executing various programs (corresponding to an example of an information processing program) stored in the memory device inside the information processing device 1, using RAM or the like as a working area. Alternatively, the control unit 3 is a controller and may be implemented by an integrated circuit such as an ASIC (Application Specific Integrated Circuit), FPGA (Field Programmable Gate Array), or GPGPU (General Purpose Graphic Processing Unit).

[0039] The memory unit 4 is implemented by, for example, semiconductor memory elements such as RAM (Random Access Memory) and flash memory, or by storage devices such as hard disks and optical discs.

[0040] User information 41 is information about the user. Figure 4 shows an example of user information 41. As shown in Figure 4, user information 41 includes items such as "User ID," "Attribute Information," "Action Information," and "Vector Information."

[0041] "User ID" is identification information that identifies a user. "Attribute information" is information about the user's attributes. Attribute information includes, for example, psychographic attributes and demographic attributes. "Action information" is information about actions that the user has intentionally taken on objects on the web. Action information includes, for example, the related information and static information mentioned above. "Vector information" is the user vector information mentioned above, which is generated by the generation unit 33 described later.

[0042] Advertisement information 42 is information about advertisements submitted by advertisers. Figure 5 shows an example of advertisement information 42. As shown in Figure 5, advertisement information 42 includes items such as "Advertisement ID," "Advertiser ID," and "Advertisement Information."

[0043] "Advertising ID" is identification information that identifies an advertisement. "Advertiser ID" is identification information that identifies the advertiser. "Advertising Information" is information related to the advertisement, including information about the content of the advertisement (overall design, images, text, etc.) and information about the advertised product or service.

[0044] Advertiser information 43 is information about the advertiser. Figure 6 shows an example of advertiser information 43. As shown in Figure 6, advertiser information 43 includes items such as "Advertiser ID" and "Advertiser Information".

[0045] The "Advertiser ID" is identification information that identifies the advertiser. The "Advertiser Information" is information about the advertiser, including the advertiser's name and the categories of products or services they sell.

[0046] Next, we will describe the functions of the control unit 3 of the information processing device 1 (acquisition unit 31, identification unit 32, generation unit 33, and provision unit 34).

[0047] The acquisition unit 31 acquires various information from the distribution server 50. Specifically, the acquisition unit 31 acquires relevant information related to the actions of the user who delivered the advertisement, as well as the regular information described above.

[0048] The identification unit 32 identifies multiple pieces of relevant information regarding actions intentionally performed by a user on an advertisement on the web. Specifically, the identification unit 32 determines whether a click operation to select a displayed advertisement was an intentional action. For example, if the user clicks the center of the area where the advertisement is displayed, the identification unit 32 determines that such a click operation is an intentional action. The identification unit 32 also determines that if the user clicks the advertisement after a certain period of time has elapsed since the advertisement was viewed (displayed), such a click operation is an intentional action. Furthermore, the identification unit 32 determines that if the user clicks the advertisement immediately after it is delivered (displayed) (within a predetermined period), such a click operation is not an intentional action (the user accidentally selected the advertisement). If the identification unit 32 determines that the action was intentional, it identifies relevant information related to that action. Related information includes, for example, identifying information of the site associated with the ad selected by the user, user input information when selecting the ad (search keywords in the case of a search service), audience categories (classification information categorized by interests, concerns, attributes, and life events from user information), and information such as the category and title (product name, tagline, etc.) of the ad selected by the user.

[0049] The generation unit 33 generates user vectors related to advertisements based on subvectors generated by individually convolving multiple pieces of related information in deep learning. Specifically, the generation unit 33 generates user vectors by aggregating subvectors generated based on related information and stationary vectors generated based on stationary information.

[0050] The provisioning unit 34 provides information based on the generated user vector to the distribution server 50 and the advertiser terminal 200. For example, the distribution server 50 selects advertisements to deliver to the user (display on the search screen) based on the user vector obtained from the provisioning unit 34. The provisioning unit 34 may also estimate, for example, whether the user will intentionally select an advertisement by clicking based on the user vector, and provide the estimation result (the probability of clicking or whether or not a click will be performed) to the distribution server 50.

[0051] Next, the processing procedure of the process executed by the information processing device 1 according to the embodiment will be described using Figure 7. Figure 7 is a flowchart showing the processing procedure of the process executed by the information processing device 1 according to the embodiment.

[0052] As shown in Figure 7, the control unit 3 first acquires various information about the user who received the advertisement from the distribution server 50 (step S101). Specifically, the control unit 3 acquires the above-mentioned related information and routine information.

[0053] Next, the control unit 3 identifies an action intentionally performed in response to the delivered advertisement (e.g., a click) and a number of related pieces of information concerning such action (step S102).

[0054] Next, the control unit 3 generates a user vector related to the advertisement based on the subvectors generated by individually convolving multiple pieces of related information in deep learning (step S103).

[0055] Next, the control unit 3 generates information based on the generated user vector and provides it to the distribution server 50 and the advertiser terminal 200 (step S104), and then terminates the process.

[0056] 〔others〕 Furthermore, some of the processes described as being performed automatically in the above embodiments can be performed manually. Alternatively, all or part of the processes described as being performed manually can be performed automatically by known methods. In addition, the processing procedures, specific names, and various data and parameters shown in the above documents and drawings can be changed at will unless otherwise specified. For example, the various information shown in each figure is not limited to the information shown.

[0057] Furthermore, the components of each illustrated device are functionally conceptual and do not necessarily need to be physically configured as shown. In other words, the specific forms of distribution and integration of each device are not limited to those shown, and all or part of them can be functionally or physically distributed and integrated in any unit according to various loads and usage conditions.

[0058] For example, some or all of the storage unit 4 shown in Figure 3 may be stored in a storage server or the like, rather than being held by each device. In this case, each device obtains various information by accessing the storage server.

[0059] [Hardware configuration] Furthermore, the information processing device 1 according to the above embodiment is realized by a computer 1000 having a configuration such as that shown in Figure 8. Figure 8 is a diagram showing an example of a hardware configuration. The computer 1000 is connected to an output device 1010 and an input device 1020, and has a configuration in which 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.

[0060] The arithmetic unit 1030 operates based on programs stored in the primary storage device 1040 and the secondary storage device 1050, as well as programs read from the input device 1020, and executes various processes. The primary storage device 1040 is a memory device, such as RAM, that temporarily stores data used by the arithmetic unit 1030 for various calculations. The secondary storage device 1050 is a storage device where data used by the arithmetic unit 1030 for various calculations and various databases are registered, and is implemented using ROM (Read Only Memory), HDD (Hard Disk Drive), flash memory, etc.

[0061] Output IF1060 is an interface for transmitting information to be output to output devices 1010, which output various types of information such as monitors and printers. It is implemented using connectors of standards such as USB (Universal Serial Bus), DVI (Digital Visual Interface), and HDMI (High Definition Multimedia Interface). Input IF1070 is an interface for receiving information from various input devices 1020, such as mice, keyboards, and scanners. It is implemented using, for example, USB.

[0062] The input device 1020 may also be a device that reads information from, for example, an optical recording medium such as a CD (Compact Disc), DVD (Digital Versatile Disc), or PD (Phase Change Rewritable Disk), a magneto-optical recording medium such as an MO (Magneto-Optical disk), tape media, magnetic recording media, or semiconductor memory. Furthermore, the input device 1020 may also be an external storage medium such as a USB memory stick.

[0063] Network IF1080 receives data from other devices via network N and sends it to the arithmetic unit 1030, and also transmits data generated by the arithmetic unit 1030 to other devices via network N.

[0064] 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.

[0065] For example, when computer 1000 functions as information processing device 1, the arithmetic unit 1030 of computer 1000 realizes the functions of control unit 3 by executing a program loaded on primary storage device 1040.

[0066] 〔effect〕 As described above, the information processing device 1 according to the embodiment comprises a specification unit 32, a generation unit 33, and a provision unit 34. The specification unit 32 identifies multiple pieces of related information relating to an action intentionally performed by a user on a target on the web. The generation unit 33 generates a user vector about the target based on subvectors generated by individually convolving the multiple pieces of related information in deep learning. The provision unit 34 provides information based on the generated user vector. With this configuration, better information can be provided.

[0067] Although some embodiments of the present invention have been described in detail above with reference to the drawings, these are illustrative examples, and the present invention can be implemented in various other forms with modifications and improvements based on the knowledge of those skilled in the art, starting with the embodiments described in the disclosure section of the invention.

[0068] 〔others〕 Furthermore, among the processes described in the above embodiments, all or part of the processes described as being performed automatically can be performed manually, or all or part of the processes described as being performed manually can be performed automatically by known methods. In addition, the processing procedures, specific names, and various data and parameters shown in the above document and drawings can be changed at will unless otherwise specified. For example, the various information shown in each figure is not limited to the information shown.

[0069] Furthermore, the components of each illustrated device are functionally conceptual and do not necessarily need to be physically configured as shown. In other words, the specific forms of distribution and integration of each device are not limited to those shown, and all or part of them can be functionally or physically distributed and integrated in any unit according to various loads and usage conditions.

[0070] Furthermore, each of the processes described in the embodiments above can be combined as appropriate, provided that the processing content does not contradict each other.

[0071] Furthermore, the terms "section, module, unit" used above can be replaced with "means" or "circuit," etc. For example, control unit 3 can be replaced with control means or control circuit. [Explanation of symbols]

[0072] 1. Information Processing Device 2 Communications Department 3. Control Unit 4 Storage section 31 Acquisition Department 32 Specific part 33 Generation part 34 Providing Department 41 User Information 42 Advertising Information 43. Advertiser Information 50 distribution servers 100 user terminals 200 advertiser terminals S Information Processing System

Claims

1. A identifying unit that detects a user's action of selecting an object on a webpage after a certain period of time has elapsed since the object was displayed, and identifies a plurality of related pieces of information related to the intentionally performed action, In deep learning, a generation unit generates a user vector relating to the target based on a subvector generated by individually convolving each of the multiple related pieces of information, A providing unit that provides information based on the generated user vector, Equipped with, The aforementioned related information is, This is information relating to the object selected by the user, The aforementioned supply unit is, Based on the user vector, the system estimates whether the user will perform the action on the target and provides the estimated result indicating whether or not the action will be performed as the information. Information processing device.

2. The generating unit is The partial vector is generated by aggregating the data from the intermediate layer, which is generated by individually convolving each of the aforementioned multiple pieces of related information. The information processing apparatus according to claim 1.

3. The specified part is, Further identify the regular information including at least the DS resource record, gender, advertising format, internet network connection, OS type, advertising style, age, location, and user terminal device type. The generating unit is The user vector is generated based on the stationary vector, which is generated by convolving the aforementioned stationary information, and the aforementioned subvector. The information processing apparatus according to claim 2.

4. The aforementioned related information is, This is information that changes in accordance with the aforementioned action. The information processing apparatus according to claim 1.

5. The generating unit is The subvector of the non-negative sparse vector representation is generated. The information processing apparatus according to claim 1.

6. The aforementioned act is, This is a click operation on the aforementioned target. The information processing apparatus according to claim 1.

7. A method of information processing performed by a computer, The process includes detecting that a user intentionally selected an object after a certain period of time has elapsed since the object was displayed on the web, and identifying multiple pieces of related information associated with the intentionally performed action, In deep learning, a generation step is to generate a user vector relating to the target based on a subvector generated by individually convolving each of the multiple related pieces of information, A provision step of providing information based on the generated user vector, Includes, The aforementioned related information is, This is information relating to the object selected by the user, The aforementioned provisioning process is, Based on the user vector, the system estimates whether the user will perform the action on the target and provides the estimated result indicating whether or not the action will be performed as the information. Information processing methods.

8. A procedure for identifying a user who has performed an action to select an object on the web after a certain period of time has elapsed since the object was displayed on the web, and for identifying a plurality of related pieces of information related to the intentionally performed action, In deep learning, a generation procedure for generating a user vector relating to the target based on a subvector generated by individually convolving the multiple pieces of related information, A procedure for providing information based on the generated user vector, Have the computer run it, The aforementioned related information is, This is information relating to the object selected by the user, The aforementioned provision procedure is, Based on the user vector, the system estimates whether the user will perform the action on the target and provides the estimated result indicating whether or not the action will be performed as the information. Information processing program.

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