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

Abstract

To establish a new way of distributing advertisements without being bound by existing ways of distributing advertisements.SOLUTION: An information processing device of the present invention comprises a selection unit and a distribution unit. The selection unit selects multiple advertisements from among advertisements posted by advertisers other than a requesting advertiser as materials for generating a merged advertisement for conveying information on the advertiser requesting generation of a merged advertisement. The distribution unit distributes a merged advertisement generated by merging the advertisements selected by the selection unit.SELECTED DRAWING: Figure 6

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, advertisement distribution via the Internet has been widely practiced. For example, in conventional advertisement distribution, advertisement content such as still images, moving images, and text for promoting a company, product, etc. is displayed in an advertisement space provided at a predetermined position on a webpage. In conventional advertisement distribution, when such advertisement content is clicked, the user is redirected to a landing page such as an advertiser's webpage or content introducing the advertisement target.

[0003] Furthermore, in conventional advertising distribution, targeted distribution may be carried out to selectively deliver advertisements that correspond to user information such as user preferences, gender, age, address, and occupation, in order to increase advertising effectiveness. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Laid-Open No. 2010-136332 Summary of the Invention [Problem to be solved by the invention]

[0005] However, conventional advertisement delivery aims to achieve a predetermined result through advertising, and new advertisement delivery methods are required that are not bound by existing methods of advertisement delivery.

[0006] The present application has been made in consideration of the above, and aims to provide an information processing device, an information processing method, and an information processing program that can realize new advertisement delivery without being bound by existing methods of advertisement delivery. [Means for solving the problem]

[0007] The information processing device according to the present application includes a selection unit and a distribution unit. The selection unit selects a plurality of advertisements from advertisements submitted by advertisers other than the requesting advertiser as materials for generating a composite advertisement for transmitting information about the requesting advertiser requesting the generation of the composite advertisement. The distribution unit distributes the composite advertisement generated by combining the advertisements selected by the selection unit. [Effects of the Invention]

[0008] According to one aspect of the embodiment, it is possible to realize a new type of advertisement delivery without being bound by existing methods of advertisement delivery. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a diagram showing an overview of a method for distributing a composite advertisement according to an embodiment. [Figure 2] FIG. 2 is a diagram showing an outline of an advertisement selection method (part 1) according to the embodiment. [Figure 3] FIG. 3 is a diagram showing an outline of an advertisement selection method (part 2) according to the embodiment. [Figure 4] FIG. 4 is a diagram showing an outline of an advertisement selection method (part 3) according to the embodiment. [Figure 5] FIG. 5 is a diagram showing an overview of an advanced version of the method for delivering a composite advertisement according to the embodiment. [Figure 6] FIG. 6 is a diagram illustrating an example of the configuration of an information processing device according to the embodiment. [Figure 7] FIG. 7 is a diagram illustrating an example of advertisement information according to the embodiment. [Figure 8] FIG. 8 is a diagram illustrating an example of distribution result information according to the embodiment. [Figure 9] FIG. 9 is a diagram illustrating an example of business type correspondence information according to the embodiment. [Figure 10] FIG. 10 is a flowchart illustrating an example of a procedure (part 1) for distributing a composite advertisement according to the embodiment. [Figure 11] FIG. 11 is a flowchart illustrating an example of a procedure (part 2) for delivering a composite advertisement according to the embodiment. [Figure 12] FIG. 12 is a flowchart illustrating an example of a procedure (part 3) for delivering a composite advertisement according to the embodiment. [Figure 13] FIG. 13 is a hardware configuration diagram illustrating an example of a computer that realizes the functions of the information processing device according to the embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0010] 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.

[0011] [1. Overview of information processing] (1-1. Synthetic Ad Delivery Method) An example of information processing according to an embodiment will be described with reference to FIG. 1. FIG. 1 is a diagram showing an overview of a synthetic advertisement delivery method according to an embodiment. Note that FIG. 1 describes an example of information processing in the case where, when a web page related to a predetermined service is provided in response to a request from a user (e.g., user U1) who is a service user, advertising content is displayed in an advertising space provided at a predetermined position on the web page. Note that the information processing according to the embodiment is not limited to the case where the advertising medium is a web page, but can also be applied in the same way to the case where the advertising medium is email.

[0012] In the example shown in FIG. 1, the information processing system SYS includes a user terminal 10 and an information processing device 100. The user terminal 10 and the information processing device 100 are connected to a network N (see, for example, FIG. 6) by wire or wirelessly. The user terminal 10 and the information processing device 100 can communicate with each other via the network N. Note that the example shown in FIG. 1 is not particularly limited, and the information processing system SYS may include more user terminals 10 and information processing devices 100 than those shown in FIG. 1.

[0013] The user terminal 10 shown in FIG. 1 is an information processing terminal used by a user U1 who is a user of various services provided by the information processing device 100. A typical example of the user terminal 10 is a smartphone, but the user terminal 10 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. In the example of FIG. 1, a smartphone is shown as the user terminal 10. In the following description, the user terminal 10 may be referred to as user U1. In other words, user U1 can be read as user terminal 10. In the following description, a user of various services provided by the information processing device 100 may be simply referred to as a "user."

[0014] The user terminal 10 also has a communication unit for connecting to the network N via a wireless communication network such as LTE (Long Term Evolution), 4G (4th Generation), or 5G (5th Generation: fifth generation mobile communication system), or via short-range wireless communication such as Bluetooth (registered trademark) or wireless LAN (Local Area Network). A user U operates the user terminal 10 having the communication unit to access the information processing device 100 and view information on various services.

[0015] Furthermore, the user terminal 10 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 user terminal 10 receives control information for realizing information display processing by the web browser, application, or the like from the information processing device 100, the user terminal 10 realizes the display processing in accordance with the received control information.

[0016] 1 is an information processing device that provides various services to a user U. The information processing device 100 is typically a server device, but 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 also be realized by a cloud system in which multiple server devices and multiple storage devices operate in cooperation with each other.

[0017] The information processing device 100 may function as a distribution device that distributes control information to user terminals 10 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). The application itself distributed from the information processing device 100 may be considered as control information.

[0018] 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.

[0019] The information processing device 100 also functions as a device for distributing advertising content. The information processing device 100 manages advertising content submitted by advertisers and manages information related to advertisement distribution results (distribution history). 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.

[0020] Meanwhile, bidding for advertisements in advertisement spaces provided on web pages is carried out using a predetermined bidding method such as RTB (Real-Time Bidding). Then, advertisements of advertisers who win the bid for the advertisement spaces are displayed in the advertisement spaces. As a result, advertisements of advertisers who lack financial resources and are not price competitive have fewer opportunities for users to view the advertisements. In view of this situation, the information processing device 100 according to the embodiment displays a composite advertisement, which combines multiple advertisements from different advertisers, in the advertisement spaces, with the aim of improving advertisement delivery from the advertiser's perspective. Hereinafter, the information processing according to the embodiment will be specifically described along with an assumed flow when the information processing is performed.

[0021] For example, the user terminal 10 transmits an access request for a predetermined service to the information processing device 100 in accordance with an operation by the user U (step S11).

[0022] When the information processing device 100 receives a request to access a predetermined service from the user terminal 10, it selects a first advertisement (hereinafter referred to as the "main advertisement") that is a main advertisement and a second advertisement (hereinafter referred to as the "subordinate advertisement") that is a subordinate advertisement (step S12). The main advertisement is a part of the advertisements that make up the composite advertisement, and is an advertisement that accepts the subordinate advertisement as part of the advertisement. The subordinate advertisement is a part of the advertisements that make up the composite advertisement, and is an advertisement that is inserted into part of the main advertisement.

[0023] For example, the information processing device 100 selects a primary advertisement and a secondary advertisement from among multiple advertisements to be combined, based on advertisement type information preset for each advertisement. The advertisement type is set by the advertiser when submitting the advertisement. As shown in FIG. 1, advertisement types include three types: a "normal advertisement," a "parasitic-allowed advertisement," and a "parasitic-wanted advertisement." The "normal advertisement" is a type selected by the advertiser when the advertiser wishes to distribute the advertisement as a conventional advertisement rather than as a combined advertisement. The "parasitic-allowed advertisement" is a type selected by the advertiser when the advertiser allows the advertisement of another advertiser to be combined as a combined advertisement. An advertisement set as a "parasitic-allowed advertisement" is treated as a primary advertisement. The "parasitic-wanted advertisement" is a type selected by the advertiser when the advertiser wishes to combine the advertisement of another advertiser as a combined advertisement. An advertisement set as a "parasitic-wanted advertisement" is treated as a secondary advertisement.

[0024] The information processing device 100 selects one primary advertisement from among advertisements whose advertisement type is "parasitic-accepting advertisement." The information processing device 100 also selects one secondary advertisement from among advertisements whose advertisement type is "parasitic-wanting advertisement." FIG. 1 shows an example in which "advertisement A" is selected as the primary advertisement and "advertisement B" is selected as the secondary advertisement. Note that the information victory device 100 may select multiple secondary advertisements when generating a composite advertisement, which means that at least one secondary advertisement is selected to be combined with the primary advertisement to generate a composite advertisement.

[0025] After the advertisement is selected, the information processing device 100 generates a composite advertisement by combining a part of the main advertisement with the secondary advertisement (step S13). In the composite advertisement generated by the information processing device 100, the main advertisement is displayed larger than the second advertisement, which is the secondary advertisement. The layout and display size of the secondary advertisement relative to the main advertisement can be determined arbitrarily, provided that at least the advertising target of the main advertisement is recognizable to the user. FIG. 1 illustrates an example of a composite advertisement AD_a in which a part of advertisement A selected as the main advertisement is combined with advertisement B selected as the secondary advertisement.

[0026] After generating the composite advertisement, the information processing device 100 distributes the generated composite advertisement to the user terminal 10 (step S14). The user terminal 10 displays the composite advertisement distributed from the information processing device 100 in an advertisement space provided on a web page corresponding to a predetermined service.

[0027] As described above, the information processing device 100 selects a primary advertisement and a secondary advertisement, generates a composite advertisement by combining a portion of the primary advertisement with the secondary advertisement, and distributes the generated composite advertisement. In this way, the information processing device 100 can improve the way advertisements are distributed from the advertiser's perspective. That is, the information processing device 100 can increase the possibility of achieving a predetermined result for the secondary advertisement by distributing an advertisement of an advertiser with low price competitiveness as a secondary advertisement by combining the advertisement with a portion of a primary advertisement with high price competitiveness.

[0028] Furthermore, the information processing device 100 may analyze the delivery results of the composite advertisement and select the main advertisement and the subordinate advertisement while avoiding combinations of advertisements for which the index value indicating the advertising effectiveness of the composite advertisement is below a predetermined standard. Furthermore, the information processing device 100 may analyze the delivery results of the composite advertisement and select the main advertisement and the subordinate advertisement while prioritizing combinations of advertisements for which the index value indicating the advertising effectiveness of the composite advertisement is equal to or greater than a predetermined standard. This allows the information processing device 100 to further increase the possibility of achieving predetermined results for the subordinate advertisements. Note that, when selecting advertisements, the information processing device 100 may select advertisements as an optimization problem, in addition to neural networks and vector search. For example, the information processing device 100 may perform optimization by combining the budgets of the main advertisement and the subordinate advertisements, the click-through rate (CTR) performance, and the like as constraints.

[0029] Furthermore, the information processing device 100 may select the primary and secondary advertisements by taking into consideration information about a combination of advertisements that should be avoided. For example, a combination of an advertisement for a car and an advertisement for an alcoholic beverage may be exemplified as a combination of advertisements that should be avoided. This allows the information processing device 100 to avoid inappropriate advertising expressions when delivering a composite advertisement.

[0030] Furthermore, the information processing device 100 may select the primary advertisement and the secondary advertisement by taking into consideration information indicating the competitive relationship between the advertisers. In this way, when distributing the composite advertisement, the information processing device 100 can take care not to erode the market share of the transaction object, such as a product or service, provided by the advertiser of the primary advertisement.

[0031] Furthermore, when a composite advertisement is selected (clicked) at a distribution destination, the information processing device 100 may determine the amount to be charged to each advertiser associated with the composite advertisement. For example, the information processing device 100 may charge only the advertiser of the main advertisement. In this case, the information processing device 100 may charge the amount to be charged to the advertiser of the main advertisement lower than the cost per click corresponding to a "normal" advertisement. Furthermore, the information processing device 100 may charge the advertiser corresponding to the clicked advertisement of the main advertisement and the secondary advertisements that constitute the composite advertisement. In this case, the information processing device 100 may prorate the sum of the cost per click of the main advertisement and the cost per click of the secondary advertisement according to the display size of the composite advertisement, and determine the prorated amount as the amount to be charged. This allows the information processing device 100 to realize a charging process tailored to the composite advertisement.

[0032] (1-2. Ad Selection Method (Part 1)) Furthermore, the information processing device 100 does not need to be particularly limited to an example in which an advertisement is selected based on an advertisement type preset by an advertiser. For example, the information processing device 100 may input information on an advertisement that is a candidate for a primary advertisement and information on an advertisement that is a candidate for a secondary advertisement, and perform advertisement selection based on the output of a trained model that outputs an estimated value of advertising effectiveness obtained by a combined advertisement obtained by combining the information on the primary advertisement and the information on the secondary advertisement. An outline of an advertisement selection method (part 1) according to an embodiment will be described below with reference to FIG. 2. FIG. 2 is a diagram illustrating an outline of an advertisement selection method (part 1) according to an embodiment.

[0033] As shown in FIG. 2, the information processing device 100 selects a main advertisement and a subordinate advertisement based on the output of the advertising effectiveness estimation model M-1, which is a trained model (step S21). The information processing device 100 causes the model to learn the characteristics of compatibility when combining the main advertisement and the subordinate advertisement based on the delivery result of the combined advertisement. As a result, the information processing device 100 inputs information about the main advertisement and the subordinate advertisement to be combined, and generates the advertising effectiveness estimation model M-1, which is a trained model that outputs an estimated value of advertising effectiveness obtained by the combined advertisement obtained by combining them. The learning for generating the advertising effectiveness estimation model M-1 will be described below.

[0034] For example, the information processing device 100 causes a model to learn the correspondence between information on the primary advertisement and the secondary advertisement, and the approval result of the sponsor of the primary advertisement with respect to the acceptance of the secondary advertisement and the score based on the advertising effectiveness of the combined advertisement.

[0035] The information about the primary and secondary advertisements may be any information related to the advertisement. For example, the information about the advertisement may be a title displayed as text related to the advertisement, a description summarizing the content of the advertisement page (web page) on which the advertisement is displayed, or information about the content of the advertisement, such as images or videos used as advertising materials. Furthermore, if the advertiser is a company, the information about the advertisement may be information such as capital and number of employees, or if the advertiser is a listed company, it may be information about the stock price.

[0036] The approval result of the primary advertisement's advertiser for accepting the secondary advertisement is a history of information indicating whether the primary advertisement's advertiser approved or denied the acceptance of the secondary advertisement as part of the primary advertisement. The advertising effectiveness of the composite advertisement is determined based on a predetermined indicator of advertising effectiveness. The predetermined indicator of advertising effectiveness can be any indicator, such as cost per acquisition (CPA), cost per click (CPC), click-through rate (CTR), number of conversions, conversion rate (CVR), or page view (PV).

[0037] For example, the administrator of the information processing device 100 presets a score that reflects the result of approval of the acceptance of the secondary advertisement by the advertiser of the primary advertisement and an index value that indicates the advertising effectiveness of the combined advertisement. Specifically, the administrator sets the score so that the score increases as the acceptance of the secondary advertisement is approved and the index value increases, and decreases when the acceptance of the secondary advertisement is rejected. Then, the administrator creates a learning data sample DT by associating a combination of information about the primary advertisement and the secondary advertisement with the set score.

[0038] The information processing device 100 uses the created learning data samples to train a model to learn the correspondence between information about the primary advertisement and the secondary advertisement, the approval result for the acceptance of the secondary advertisement by the advertiser of the primary advertisement, and a score based on the advertising effectiveness of the combined advertisement. The information processing device 100 can train the model to learn the above-mentioned correspondence using any network such as a neural network as the learning model. Furthermore, when using a neural network as the learning model, the information processing device 100 performs a learning process to adjust the values ​​of weights (connection coefficients) that are taken into account when scores (values) are propagated between nodes in each layer that make up the neural network. For example, the information processing device 100 uses any method such as backpropagation (error backpropagation method) to perform a process to optimize (correct) parameters (connection coefficients) so as to reduce the error between the output from the learning model and the correct answer (correct answer data) corresponding to the input. When the information processing device 100 uses the backpropagation technique, it can correct the parameters (connection coefficients) to reduce the error between the output from the learning model and the correct answer (correct answer data) corresponding to the input by performing a learning process to minimize a predetermined loss function.

[0039] Through the above-mentioned learning process, the information processing device 100 can generate an advertising effectiveness estimation model M-1, which is a trained model that inputs information about the primary advertisement and secondary advertisement to be combined and outputs an estimated value of the advertising effectiveness obtained by the combined advertisement.

[0040] The information processing device 100 inputs all combinations of primary advertisements and secondary advertisements to be combined into the advertising effectiveness estimation model M-1, and determines the primary advertisements and secondary advertisements to be selected as combination targets based on the estimated advertising effectiveness output for each combination from the advertising effectiveness estimation model M-1. For example, the information processing device 100 determines the primary advertisements and secondary advertisements that constitute the combination with the largest estimated advertising effectiveness as the combination targets. Figure 2 shows an example in which "Advertisement A" is selected as the primary advertisement and "Advertisement B" is selected as the secondary advertisement.

[0041] Then, the information processing device 100 generates a composite advertisement using the main advertisement and the subordinate advertisement selected as the objects to be combined based on the output from the advertising effectiveness estimation model M-1 (step S22). For example, the information processing device 100 generates a composite advertisement by combining a part of the main advertisement with the subordinate advertisement using the main advertisement and the subordinate advertisement selected as the objects to be combined. Fig. 2 illustrates an example of a composite advertisement AD_a in which a part of advertisement A selected as the main advertisement is combined with advertisement B selected as the subordinate advertisement.

[0042] As described above, the information processing device 100 can estimate the advertising effectiveness obtained by a combined advertisement obtained by combining a main advertisement and a subordinate advertisement selected as targets for combination, and can realize delivery of a highly effective combined advertisement.

[0043] (1-3. Ad Selection Method (Part 2)) Furthermore, the information processing device 100 may input information about the advertisers of the primary advertisement and the secondary advertisements to be combined, and perform advertisement selection based on the output of a trained model that vectorizes the primary advertisement and the secondary advertisements, respectively. Hereinafter, an outline of an advertisement selection method (part 2) according to an embodiment will be described with reference to FIG. 3. FIG. 3 is a diagram illustrating an outline of an advertisement selection method (part 2) according to an embodiment.

[0044] As shown in FIG. 3, the information processing device 100 selects a primary advertisement and a secondary advertisement based on the output of the advertisement vector output model M-2, which is a trained model (step S31). The information processing device 100 trains a model that vectorizes each advertisement based on information about the advertisers of each advertisement that constitutes the combined advertisement and the delivery result of the combined advertisement. As a result, the information processing device 100 inputs information about the primary advertisement and the secondary advertisement to be combined, and generates the advertisement vector output model M-2, which is a trained model that outputs a feature vector obtained by vectorizing the features of the information about the primary advertisement and a feature vector obtained by vectorizing the features of the information about the secondary advertisement. The learning for generating the advertisement vector output model M-2 is described below.

[0045] For example, the administrator of the information processing device 100 sets a score based on information about the advertisers of the primary advertisement and the secondary advertisement, the approval result of the primary advertisement's advertiser for accepting the secondary advertisement, and the advertising effectiveness of the combined advertisement.

[0046] The information about the advertisers of the primary and secondary advertisements may be any information related to the advertisements. For example, if the advertiser is a company, the information may be information such as capital and number of employees, and if the advertiser is a listed company, the information may be information about the stock price. Note that the information about the advertisers of the primary and secondary advertisements may be used when setting the score. In this case, the information about the advertisement may be information about the title displayed as text related to the advertisement, a description summarizing the content of the advertisement page (web page) on which the advertisement is displayed, or the content of the advertisement, such as images and videos used as advertising materials.

[0047] The approval result of the primary advertisement's advertiser for accepting the secondary advertisement is a history of information indicating whether the primary advertisement's advertiser approved or denied the acceptance of the secondary advertisement as part of the primary advertisement. The advertising effectiveness of the composite advertisement is determined based on a predetermined indicator of advertising effectiveness. The predetermined indicator of advertising effectiveness can be any indicator, such as cost per acquisition (CPA), cost per click (CPC), click-through rate (CTR), number of conversions, conversion rate (CVR), or page view (PV).

[0048] For example, the administrator of the information processing device 100 presets a score that reflects the result of approval of the acceptance of the secondary advertisement by the advertiser of the primary advertisement and an index value that indicates the advertising effectiveness of the combined advertisement. Specifically, the administrator sets the score so that the score increases as the acceptance of the secondary advertisement is approved and the index value increases, and decreases when the acceptance of the secondary advertisement is rejected. Then, the administrator creates a learning data sample DT by associating the set score with a combination of information about the advertisers of the primary advertisement and the secondary advertisement.

[0049] The information processing device 100 uses the created training data sample DT to train a model such that the larger the score set for a combination of information about the advertisers of the primary advertisement and the secondary advertisement, the smaller the similarity between the feature vector corresponding to the advertiser of the primary advertisement and the feature vector corresponding to the advertiser of the secondary advertisement is output as a vector. Specifically, the information processing device 100 trains a model that outputs each feature vector such that the larger the set score in the training data sample DT, the smaller the cosine similarity between the feature vector corresponding to the primary advertisement and the feature vector corresponding to the secondary advertisement.

[0050] For example, when the information processing device 100 uses an arbitrary network such as a neural network as a learning model, it performs a learning process to adjust the values ​​of weights (connection coefficients) that are taken into account when scores (values) are propagated between nodes in each layer that constitute the neural network. For example, the information processing device 100 uses an arbitrary method such as backpropagation (error backpropagation) to perform a process to optimize (correct) parameters (connection coefficients) so as to reduce the error between the output from the learning model and the correct answer (correct answer data) corresponding to the input. In other words, the information processing device 100 performs a process to optimize parameters (connection coefficients) so that the cosine similarity between the feature vector corresponding to the primary advertisement and the feature vector corresponding to the secondary advertisement decreases as the set score in the learning data sample DT increases. Note that when the information processing device 100 uses the backpropagation method, it can correct the parameters (connection coefficients) so as to reduce the error between the output from the learning model and the correct answer (correct answer data) corresponding to the input by performing a learning process to minimize a predetermined loss function.

[0051] Through the above-described learning process, the information processing device 100 can generate an advertisement vector output model M-2, which is a trained model that vectorizes each advertisement based on information about the advertisers of each advertisement that constitutes the composite advertisement and the delivery results of the composite advertisement.

[0052] For each combination of a primary advertisement and a secondary advertisement to be combined, the information processing device 100 inputs information about the advertiser into the advertisement vector output model M-2, and determines the primary advertisement and the secondary advertisement to be selected as the combination targets based on the similarity between the feature vectors output from the advertisement vector output model M-2. For example, the information processing device 100 determines the primary advertisement and the secondary advertisement to be combined based on the smallest similarity between the feature vectors. Figure 3 shows an example in which "Advertisement A" is selected as the primary advertisement and "Advertisement B" is selected as the secondary advertisement.

[0053] Then, the information processing device 100 generates a composite advertisement using the main advertisement and the subordinate advertisement selected as the objects to be combined (step S32). For example, the information processing device 100 generates a composite advertisement by combining a part of the main advertisement with the subordinate advertisement using the main advertisement and the subordinate advertisement selected as the objects to be combined. Fig. 3 illustrates an example of a composite advertisement AD_a in which a part of advertisement A selected as the main advertisement is combined with advertisement B selected as the subordinate advertisement.

[0054] Furthermore, the information processing device 100 may train a model so that the closer the scale of the advertisers, the smaller the similarity between the vectors corresponding to the advertisements that make up the composite advertisement. For example, when creating a training data sample DT, the administrator of the information processing device 100 reflects the scale (capital, number of employees, stock price, etc.) of the advertiser companies in the set score. Specifically, the administrator adjusts the set score so that the closer the scale of the advertiser companies, the smaller the set score. Then, the information processing device 100 performs a training process using the training data sample DT in which the scale of the advertisers is reflected.

[0055] Furthermore, the information processing device 100 may train a model so that the more the composite advertisement is selected (clicked), the smaller the similarity between the vectors corresponding to the advertisements that make up the composite advertisement becomes. For example, when creating a learning data sample DT, the administrator of the information processing device 100 reflects an index value (such as a click rate or a conversion rate) indicating the advertising effectiveness in the set score. Specifically, the administrator adjusts the set score so that the larger the index value of the advertising effectiveness, the smaller the set score becomes. Then, the information processing device 100 performs a learning process using the learning data sample DT that reflects the size of the advertiser.

[0056] As described above, the information processing device 100 can verify the similarity between the advertiser of the primary advertisement and the advertiser of the secondary advertisement selected as the merging target, thereby realizing the delivery of a highly effective merged advertisement. That is, by selecting advertisements of advertisers with different scales as the merging target, the information processing device 100 can increase the appeal of the merged advertisement to users, and can expect high advertising effectiveness. Furthermore, the information processing device 100 can expect high advertising effectiveness by selecting advertisements that match a combination of advertisers with high advertising effectiveness as the merging target.

[0057] (1-4. Ad Selection Method (Part 3)) Furthermore, the information processing device 100 may automatically select a primary advertisement and a secondary advertisement from among a plurality of advertisements to be combined in accordance with a predetermined selection rule, regardless of the advertisement type described in the advertisement selection method (part 1). An outline of the advertisement selection method (part 3) according to the embodiment will be described below with reference to FIG. 4. FIG. 4 is a diagram showing an outline of the advertisement selection method (part 3) according to the embodiment.

[0058] As shown in FIG. 4, the information processing device 100 automatically selects a main advertisement and a secondary advertisement from among multiple advertisements to be combined, based on the delivery results of the combined advertisement generated by combining a part of the main advertisement with a secondary advertisement (step S41).

[0059] FIG. 4 illustrates an example in which, as a result of delivery of a composite advertisement, the click-through rate indicating the advertising effectiveness of a composite advertisement composed of a main advertisement A and a secondary advertisement B is equal to or greater than a threshold value TH1. FIG. 4 also illustrates that the target category to which the advertising target of main advertisement A belongs is category CT1, and the target category to which the advertising target of secondary advertisement B belongs is category CT2. FIG. 4 also illustrates an example in which, as a result of delivery of a composite advertisement, the click-through rate indicating the advertising effectiveness of a composite advertisement composed of main advertisement C and secondary advertisement D is less than a threshold value TH1. FIG. 4 also illustrates that the target category to which the advertising target of main advertisement C belongs is category CT3, and the target category to which the advertising target of secondary advertisement D belongs is category CT4.

[0060] The information processing device 100 selects a main advertisement and a subordinate advertisement while avoiding a combination of advertisements that constitute a combined advertisement for which an index value indicating the advertising effectiveness of the combined advertisement does not satisfy a predetermined standard. Specifically, the information processing device 100 first identifies target categories for selecting the main advertisement and the subordinate advertisement to be combined. For example, in the case shown in FIG. 4, the information processing device 100 identifies a category CT1 to which the advertising target of advertisement A that constitutes the combined advertisement and whose click-through rate is equal to or greater than a threshold value TH1 belongs as the target category for selecting the main advertisement, and similarly identifies a category CT2 to which the advertising target of advertisement B belongs as the target category for selecting the subordinate advertisement.

[0061] Next, the information processing device 100 acquires a primary advertisement and a secondary advertisement that satisfy a predetermined condition from among a plurality of advertisements that belong to the target category. For example, the information processing device 100 may select a first advertisement and a second advertisement based on the amount of advertising expenses associated with each advertisement. In the case shown in FIG. 4, the information processing device 100 randomly selects, as a primary advertisement, an advertisement that satisfies the condition that the amount of advertising expenses is equal to or greater than a threshold value TH2 from among a plurality of advertisements whose advertising targets belong to category CT1. Also, in the case shown in FIG. 4, the information processing device 100 randomly selects, as a secondary advertisement, an advertisement that satisfies the condition that the amount of advertising expenses is less than a threshold value TH2 from among a plurality of advertisements whose advertising targets belong to category CT2.

[0062] Furthermore, the information processing device 100 may select a primary advertisement and a secondary advertisement based on information indicating the degree to which the advertiser of each advertisement is recognized (hereinafter referred to as "recognition"). In the case shown in FIG. 4, the information processing device 100 randomly selects, as a primary advertisement, an advertisement that satisfies the condition that the recognition level is equal to or higher than a threshold value TH3 from among a plurality of advertisements whose advertising targets belong to category CT1. In the case shown in FIG. 4, the information processing device 100 randomly selects, as a secondary advertisement, an advertisement that satisfies the condition that the recognition level is lower than a threshold value TH3 from among a plurality of advertisements whose advertising targets belong to category CT2.

[0063] Then, the information processing device 100 generates a composite advertisement using the primary advertisement and the secondary advertisement selected in step S41 (step S42). For example, the information processing device 100 generates a composite advertisement by using the primary advertisement and the secondary advertisement selected as the objects of composition to combine the secondary advertisement with a part of the primary advertisement. Fig. 4 illustrates an example of a composite advertisement AD_a in which advertisement B selected as a secondary advertisement is combined with a part of advertisement A selected as the primary advertisement.

[0064] Furthermore, the information processing device 100 may select the primary advertisement and the secondary advertisements by taking into consideration information about a combination of advertisements that should be avoided. This allows the information processing device 100 to avoid inappropriate advertising expressions when delivering a composite advertisement.

[0065] Furthermore, the information processing device 100 may select the primary advertisement and the secondary advertisement by taking into consideration information indicating the competitive relationship between the advertisers. In this way, when distributing the composite advertisement, the information processing device 100 can take care not to erode the market share of the transaction object, such as a product or service, provided by the advertiser of the primary advertisement.

[0066] (1-5. Advanced synthetic ad delivery methods) Furthermore, the information processing device 100 may select advertisements for conveying information about advertisers different from the other advertisers from among advertisements submitted by the other advertisers, and distribute a composite advertisement generated by combining the selected advertisements. This allows the information processing device 100 to realize novel advertisement distribution without being bound by existing methods of advertisement distribution. Below, an overview of an advanced version of the composite advertisement distribution method according to the embodiment will be described with reference to FIG. 5. FIG. 5 is a diagram showing an overview of an advanced version of the composite advertisement distribution method according to the embodiment.

[0067] As shown in Fig. 5, the advertiser U2_D uses the advertiser terminal 20 to send a composite advertisement generation request to the information processing device 100 (step S51). Fig. 5 shows an example in which the industry of the business run by the advertiser U2_D is "industry X4." Note that the industry can be set at any granularity.

[0068] The advertiser terminal 20 shown in FIG. 5 is an information processing terminal used by an advertiser U2_D who is a user of various services provided by the information processing device 100. The advertiser terminal 20 may be a smartphone, a tablet terminal, a notebook PC (Personal Computer), a desktop PC, a mobile phone, a PDA (Personal Digital Assistant), or the like. In the example of FIG. 5, a smartphone is shown as the advertiser terminal 20. In the following description, the advertiser terminal 20 may be referred to as the advertiser U2_D. In other words, the advertiser U2_D can be read as the advertiser terminal 20.

[0069] The advertiser terminal 20 also has a communication unit for connecting to a network N (see FIG. 6) via a wireless communication network such as LTE (Long Term Evolution), 4G (4th Generation), or 5G (5th Generation: fifth generation mobile communication system), or via short-range wireless communication such as Bluetooth (registered trademark) or wireless LAN (Local Area Network). The advertiser U2_D operates the advertiser terminal 20 having the communication unit to access the information processing device 100 and use various services.

[0070] Furthermore, the advertiser terminal 20 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 advertiser terminal 20 receives control information for realizing the display processing of information by a web browser, an application, or the like from the information processing device 100, the advertiser terminal 20 realizes the display processing in accordance with the received control information.

[0071] On the other hand, when the information processing device 100 receives a composite advertisement generation request from the advertiser U2_D, the information processing device 100 selects multiple advertisements from advertisements submitted by advertisers other than the requesting advertiser U2_D as materials for generating a composite advertisement for conveying information about the requesting advertiser U2_D requesting the generation of the composite advertisement (step S52). For example, the information processing device 100 identifies a related industry associated with the industry corresponding to the requesting advertiser requesting the generation of the composite advertisement based on a predetermined industry correspondence relationship. FIG. 5 shows industry correspondence information indicating the industry correspondence relationship. According to the industry correspondence information shown in FIG. 5, an example is shown in which the industry of the business run by the advertiser U2_A is "industry X1," the industry of the business run by the advertiser U2_B is "industry X2," the industry of the business run by the advertiser U2_C is "industry X3," and the industry of the business run by the advertiser U2_D is "industry X4." In the case shown in Figure 5, the information processing device 100 refers to industry correspondence information that indicates the correspondence relationship between pre-defined industries, and identifies "industry X1, industry X2" as the related industries that are associated with the industry: "industry X4" that corresponds to the requesting advertiser U2_D that requests the generation of a composite advertisement.

[0072] Next, the information processing device 100 selects advertisements to be combined from advertisements input by advertisers associated with the identified business type. Fig. 5 shows an example in which advertisements are submitted by advertisers U2_A, U2_B, and U2_C, which are different from the advertiser U2_D that has requested the generation of the combined advertisement. Note that the advertisement may be input by the advertiser U2_D that has sent the request to generate the combined advertisement.

[0073] 5, the information processing device 100 selects advertisements to be combined from advertisements submitted by advertiser U2_A linked to "industry X1" identified as an associated industry of "industry X4" corresponding to advertiser U2_D, and advertisements submitted by advertiser U2_B linked to "industry X2" identified as an associated industry of "industry X4" corresponding to advertiser U2_D. In the example shown in FIG. 5, the information processing device 100 has identified a plurality of associated industries (for example, industry X1 and industry X2) associated with the industry corresponding to advertiser U2_D, the requester requesting the generation of the combined advertisement. Therefore, the information processing device 100 selects advertisements to be combined from each of the advertisements submitted by advertisers linked to each of the identified industries. That is, the information processing device 100 selects advertisements to be combined from advertisements submitted by advertiser U2_A associated with "industry X1," and also selects advertisements to be combined from advertisements submitted by advertiser U2_B associated with "industry X2." FIG. 5 shows an example in which "Advertisement A-1" is selected from advertisements submitted by advertiser U2_A, and "Advertisement B-1" is selected from advertisements submitted by advertiser U2_B. Note that if only one related industry associated with the industry corresponding to advertiser U2_D, which is the requester requesting the generation of a combined advertisement, is identified, multiple advertisements to be combined may be selected from advertisements submitted by advertisers associated with the identified industry.

[0074] Furthermore, the information processing device 100 generates a composite advertisement by combining the advertisements selected in step S52 (step S53). Fig. 5 illustrates a composite advertisement AD_b obtained by combining "advertisement A-1" and "advertisement B-1". Then, the information processing device 100 delivers the composite advertisement generated in step S53 to the user U1 (step S54).

[0075] Furthermore, when the advertisement materials of the advertisements to be combined are images, the information processing device 100 may generate the combined advertisement using a GAN (Generative Adversarial Network).

[0076] In addition, when advertiser U2_D, which sent a request to generate a composite advertisement, achieves a predetermined result, the information processing device 100 may return a reward according to the predetermined result to advertiser U2_A of "Advertisement A-1" and advertiser U2_B of "Advertisement B-1," which were selected as advertisements to convey information about advertiser U2_D.

[0077] Furthermore, the information processing device 100 may change the advertisement to be selected depending on the user attribute (context) to which the composite advertisement is to be delivered. Furthermore, the information processing device 100 may change the landing page to be delivered to the user depending on the user attribute (context) of the user who selected (clicked) the composite advertisement.

[0078] Note that FIG. 5 illustrates an example of information processing in which the information processing device 100 identifies a related industry associated with the industry corresponding to the advertiser that made the request to generate a composite advertisement based on the correspondence relationship between the advertiser's industry and the related industry, and selects advertisements to be combined from advertisements submitted by the advertiser associated with the identified industry. However, this case is not particularly limited. The industry is an example of information that can identify the correspondence relationship between advertisers. Processing can be performed using any information other than the industry as long as it can identify the correspondence relationship between advertisers. For example, the information processing device 100 may perform processing based on a category arbitrarily set for each advertiser. Specifically, the information processing device 100 can identify a related category associated with a category corresponding to the advertiser that made the request to generate a composite advertisement based on the category correspondence relationship previously set for each advertiser, and select advertisements to be combined from advertisements submitted by the advertiser associated with the identified related category. Furthermore, when multiple related categories are identified, the information processing device 100 can select advertisements to be combined from each of the advertisements submitted by the advertiser associated with each of the identified related categories.

[0079] [2. Configuration of information processing device] An information processing device 100 according to an embodiment will be described with reference to Fig. 6. Fig. 6 is a diagram showing an example of the configuration of the information processing device according to an embodiment. As shown in Fig. 6, the information processing device 100 includes a communication unit 110, a storage unit 120, and a control unit 130.

[0080] (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 user terminal 10 and the advertiser terminal 20 via the network N.

[0081] (Regarding the storage unit 120) The storage unit 120 is realized by, for example, a semiconductor memory element such as a RAM (Random Access Memory) or a flash memory, or a storage device such as a hard disk or an optical disk. The storage unit 120 has an advertisement information storage unit 121, a distribution result information storage unit 122, an advertising effectiveness estimation model storage unit 123, an advertisement vector output model storage unit 124, and an industry correspondence information storage unit 125.

[0082] The storage unit 120 can store, as information other than that shown in Fig. 6, information on combinations of advertisements that should be avoided as information to be referenced when selecting multiple advertisements to be combined, information indicating competitive relationships between advertisers, etc. The storage unit 120 can also store, as information other than that shown in Fig. 6, selection rules for automatically selecting a primary advertisement and a secondary advertisement from multiple advertisements to be combined.

[0083] (Advertisement information storage unit 121) The advertisement information storage unit 121 stores advertisement information related to advertisements. FIG. 7 is a diagram showing an example of advertisement information according to the embodiment. Note that FIG. 7 shows an example of advertisement information according to the embodiment, and the advertisement information may include information other than that shown in FIG. 7. Also, FIG. 7 shows an example in which conceptual information is displayed for multiple items included in the advertisement information, but in reality, specific information such as numerical values ​​and character strings is stored.

[0084] 7, the advertising information stored in the advertising information storage unit 121 has a plurality of items such as an "advertising ID," an "advertiser ID," an "advertising detail information," an "category information," an "associated keyword information," an "advertising type," an "advertising budget," an "awareness," an "industry," etc. These items in the advertising information are associated with each other.

[0085] The "advertising ID" field stores identification information (advertising ID) for identifying an advertisement. In the case of a composite advertisement, the "advertising ID" field may also store one advertising ID assigned to the composite advertisement. In the case of a composite advertisement, the "advertising ID" field may also store advertising IDs assigned to each advertisement constituting the composite advertisement.

[0086] The "Advertiser ID" field stores identification information (advertiser ID) for identifying the advertiser who submitted the advertisement. In the case of a composite advertisement, the "Advertiser ID" field may store the advertiser IDs of each advertisement that constitutes the combined advertisement.

[0087] The "detailed advertisement information" field stores detailed information about the content of the advertisement. For example, the detailed information may include a title displayed as text related to the advertisement, a description summarizing the content of the advertisement page (web page) on which the advertisement is displayed, and a uniform resource locator (URL) of the link destination set in the displayed advertisement.

[0088] The "category information" field stores information indicating the target category to which the advertising target of the advertisement belongs. The target category is indicated by a division that groups identical or similar products or identical or similar services at a predetermined granularity.

[0089] The "related keyword information" field stores information indicating related keywords related to the advertisement. For example, for an advertisement about automobiles, related keywords include words that tend to co-occur in search queries, such as "new car" and "sales."

[0090] The "Advertisement type" field stores information indicating the type of advertisement set by the advertiser when submitting the advertisement. Advertisement types include three types: "Normal," "Parasitic Allowed," and "Parasitic Desired." "Normal" is a type selected by an advertiser when it wishes to be delivered as a conventional advertisement rather than as a composite advertisement. "Parasitic Allowed" is a type selected by an advertiser when it wishes to allow the advertisement of another advertiser to be combined as a composite advertisement. An advertisement with "Parasitic Allowed" set is treated as a primary advertisement. "Parasitic Desired" is a type selected by an advertiser when it wishes to be combined as a composite advertisement with the advertisement of another advertiser. An advertisement with "Parasitic Desired" set is treated as a secondary advertisement.

[0091] The "advertising budget" field stores information indicating the advertising budget (advertising expenses) set by the advertiser when submitting the advertisement.

[0092] The "recognition" field stores information indicating the degree to which the advertiser is recognized by users (recognition). The recognition stored in the "recognition" field may be predetermined based on, for example, the number of followers on a social networking service (SNS), the number of searches on a search service, or information collected by any method, such as a user survey or crowdsourcing, regarding each advertiser.

[0093] The "industry" field stores information indicating the type of business that the advertiser is running. The industry is indicated by a classification that compiles the business that the advertiser is running at a predetermined granularity.

[0094] For example, according to Fig. 7, the advertiser of the advertising information identified by the advertising ID: "advertisement #01" is identified by "advertiser #01." Also, according to Fig. 7, information mutually associated with the advertising information identified by the advertising ID: "advertisement #01" includes "advertisement identification information EX01," "category information #01," "related keyword information #01," "advertisement type EX01," "advertising budget EX01," "recognition EX01," and "industry EX01."

[0095] (Distribution result information storage unit 122) The distribution result information storage unit 122 stores distribution result information relating to the distribution results of the advertisement. Fig. 8 is a diagram showing an example of the distribution result information according to the embodiment.

[0096] Note that Fig. 8 illustrates an example of the delivery result information according to the embodiment, and may include information other than that illustrated in Fig. 8. For example, the delivery result information storage unit 122 may store identification information (advertiser ID) for identifying an advertiser, a cost per click (CPC), a click-through rate (CTR), the number of conversions, page views (PV), or other predetermined index values ​​indicating advertising effectiveness.

[0097] Also, while Figure 8 shows an example in which conceptual information is displayed for multiple items in the distribution result information, in reality, specific information such as numerical values ​​or character strings corresponding to each item is stored.

[0098] 8, the delivery result information stored in the delivery result information storage unit 122 has a plurality of items such as an "advertising ID" item, an "impression count" item, an "number of clicks" item, a "conversion rate" item, etc. These items in the delivery result information are associated with each other.

[0099] The "advertising ID" field stores identification information (advertising ID) for identifying an advertisement. Note that the identification information (advertising ID) stored in the "advertising ID" field of the distribution result information may be the same as the identification information (advertising ID) stored in the "advertising ID" field of the advertisement information shown in FIG. 7.

[0100] The "Number of impressions" field stores information indicating the number of times an advertisement was displayed (number of impressions). The "Number of clicks" field stores information indicating the number of times an advertisement was clicked. The "Conversion rate" field stores information indicating the percentage of users who accessed a specified web page and achieved a specified result (conversion).

[0101] For example, Figure 8 shows that the number of times an advertisement identified by the advertisement ID: "Advertisement #01" has been displayed is "Number of Display Times EX01", the number of clicks is "Number of Clicks EX01", and the conversion rate is "Conversion Rate EX01".

[0102] (Advertising effect estimation model storage unit 123) The advertising effectiveness estimation model storage unit 123 stores information about an advertising effectiveness estimation model, which is a trained model that inputs information about the primary advertisement and the secondary advertisement to be combined and outputs an estimated value of the advertising effectiveness obtained by the combined advertisement obtained by combining them.

[0103] The information related to the advertising effectiveness estimation model stored in the advertising effectiveness estimation model storage unit 123 includes various information constituting the model, such as information on the configuration (network configuration) of the model and information on parameters. That is, the various information constituting the model includes information on nodes in each layer of the network, functions employed by each node, connection relationships between nodes, and connection coefficients set for connections between nodes.

[0104] (Advertising vector output model storage unit 124) The advertising vector output model storage unit 124 inputs information about the primary advertisement and the secondary advertisement to be synthesized, and stores information about the advertising vector output model, which is a trained model that outputs a feature vector obtained by vectorizing the features of the information about the primary advertisement and a feature vector obtained by vectorizing the features of the information about the secondary advertisement.

[0105] The information about the advertising vector output model stored in the advertising vector output model storage unit 124 includes various information constituting the model, such as information about the model configuration (network configuration) and information about parameters. That is, the various information constituting the model includes information about the nodes in each layer of the network, the functions employed by each node, the connection relationships between the nodes, and the connection coefficients set for the connections between the nodes.

[0106] (Industry correspondence information storage unit 125) The business type correspondence information storage unit 125 stores information indicating a correspondence relationship between predefined business types and information on related business types related to the business type of the business run by the advertiser. Fig. 9 is a diagram showing an example of the business type correspondence information according to the embodiment.

[0107] 9, the business type correspondence information stored in the business type correspondence information storage unit 125 has a plurality of items such as an “business type” item and an “associated business type” item. These items in the business type correspondence information are associated with each other.

[0108] The "industry" field stores information indicating the type of business that the advertiser operates as a business, classified into a predetermined granularity. The "related industry" field stores information indicating related industries that have a certain relevance to the industry stored in the "industry" field.

[0109] 9, for example, the related industries of "Industry 1" are shown to be "Industry 4, Industry 5." When the information processing device 100 receives a composite advertisement generation request from an advertiser, it can identify related industries that are related to the industry corresponding to the advertiser by referring to the industry correspondence information.

[0110] (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).

[0111] As shown in Fig. 6, control unit 130 has selection unit 131, generation unit 132, distribution unit 133, determination unit 134, and learning unit 135, and realizes or executes the functions and actions of information processing described below. Note that the internal configuration of control unit 130 is not limited to the configuration shown in Fig. 6, and may have other configurations as long as they perform the information processing described below. Furthermore, the connection relationship between each processing unit included in control unit 130 is not limited to the connection relationship shown in Fig. 6, and may be other connection relationships.

[0112] (Selection unit 131) The selection unit 131 selects a primary advertisement and a secondary advertisement. The selection unit 131 can select a primary advertisement and a secondary advertisement from among a plurality of advertisements to be combined, based on information about advertisement types preset for each advertisement by the advertiser. The selection unit 131 can select a primary advertisement and a secondary advertisement while avoiding a combination of advertisements in which an index value indicating the advertising effectiveness of the combined advertisement is less than a predetermined standard. The selection unit 131 can select a primary advertisement and a secondary advertisement while giving priority to a combination of advertisements in which an index value indicating the advertising effectiveness of the combined advertisement is equal to or greater than a predetermined standard. The selection unit 131 can select a primary advertisement and a secondary advertisement while taking into account information about a combination of advertisements whose combination should be avoided. The selection unit 131 can select a primary advertisement and a secondary advertisement while taking into account information indicating a competitive relationship between advertisers.

[0113] The selection unit 131 can also select a primary advertisement and a secondary advertisement based on the output of a trained model trained by the learning unit 135. For example, the selection unit 131 can select a primary advertisement and a secondary advertisement to be combined based on the output from an advertising effectiveness estimation model, which is a trained model trained by the learning unit 135. For example, the selection unit 131 can also select a primary advertisement and a secondary advertisement to be combined based on the similarity between feature vectors output from an advertising vector output model, which is a trained model trained by the learning unit 135.

[0114] The selection unit 131 can also select a primary advertisement and a secondary advertisement from among multiple advertisements to be combined, based on the delivery result of the combined advertisement, in accordance with a predetermined selection rule. For example, the selection unit 131 can select a primary advertisement and a secondary advertisement while avoiding a combination of advertisements that constitute a combined advertisement for which an index value indicating the advertising effectiveness of the combined advertisement does not satisfy a predetermined standard. For example, the selection unit 131 can select a primary advertisement and a secondary advertisement based on the amount of advertising expenses associated with each advertisement. For example, the selection unit 131 can select a primary advertisement and a secondary advertisement based on information indicating the degree to which the advertiser of each advertisement is recognized.

[0115] Furthermore, the selection unit 131 can select multiple advertisements from advertisements submitted by advertisers other than the requesting advertiser as materials for generating a composite advertisement for transmitting information about the requesting advertiser requesting the generation of the composite advertisement. For example, the selection unit 131 can identify related categories associated with the category corresponding to the requesting advertiser based on the correspondence between categories (for example, industry) previously set for each advertiser, and select advertisements to be combined from advertisements submitted by advertisers linked to the identified related categories. Furthermore, for example, when multiple related categories are identified, the selection unit 131 can select advertisements to be combined from each of the advertisements submitted by advertisers linked to each of the identified related categories.

[0116] (Generation unit 132) The generation unit 132 generates a composite advertisement by combining a part of the main advertisement with the secondary advertisement, based on the main advertisement and the secondary advertisement selected by the selection unit 131. The generation unit 132 can arbitrarily determine the layout and display size of the secondary advertisement relative to the main advertisement, provided that at least the advertising target of the main advertisement is recognizable to the user.

[0117] (Distribution Section 133) The distribution unit 133 distributes the composite advertisement generated by the generation unit 132 through the communication unit 110. The distribution unit 133 collects distribution results of the composite advertisement and registers distribution result information related to the collected distribution results in the distribution result information storage unit 122.

[0118] (Decision unit 134) When a composite advertisement is selected, the determination unit 134 determines the amount to be charged to each advertiser associated with the composite advertisement. For example, the determination unit 134 may charge only the advertiser of the main advertisement. In this case, the determination unit 134 may set the amount to be charged to the advertiser of the main advertisement lower than the cost per click corresponding to a "normal" advertisement. Furthermore, the determination unit 134 may charge the advertiser corresponding to the clicked advertisement of the main advertisement and the secondary advertisements that constitute the composite advertisement. In this case, the determination unit 134 may prorate the sum of the cost per click of the main advertisement and the cost per click corresponding to the secondary advertisement according to the display size of the composite advertisement, and determine the prorated amount as the amount to be charged.

[0119] (Learning Section 135) The learning unit 135 causes the model to learn characteristics of compatibility when combining a main advertisement and a subordinate advertisement based on the delivery result of the combined advertisement. For example, the learning unit 135 causes the model to learn the correspondence between information about the main advertisement and the subordinate advertisement, and the approval result of the advertiser of the main advertisement for accepting the subordinate advertisement and the score based on the advertising effectiveness of the combined advertisement. As a result, the learning unit 135 inputs information about the main advertisement and the subordinate advertisement to be combined, and generates an advertising effectiveness estimation model, which is a trained model that outputs an estimated value of the advertising effectiveness obtained by the combined advertisement obtained by combining them.

[0120] The information about the primary and secondary advertisements may be any information related to the advertisement. For example, the information about the advertisement may be a title displayed as text related to the advertisement, a description summarizing the content of the advertisement page (web page) on which the advertisement is displayed, or information about the content of the advertisement, such as images or videos used as advertising materials. Furthermore, if the advertiser is a company, the information about the advertisement may be information such as capital and number of employees, or if the advertiser is a listed company, it may be information about the stock price.

[0121] The approval result of the primary advertisement's advertiser for accepting the secondary advertisement is a history of information indicating whether the primary advertisement's advertiser approved or denied the acceptance of the secondary advertisement as part of the primary advertisement. The advertising effectiveness of the composite advertisement is determined based on a predetermined indicator of advertising effectiveness. The predetermined indicator of advertising effectiveness can be any indicator, such as cost per acquisition (CPA), cost per click (CPC), click-through rate (CTR), number of conversions, conversion rate (CVR), or page view (PV).

[0122] For example, the administrator of the information processing device 100 pre-sets a score that reflects the result of approval of the acceptance of the secondary advertisement by the advertiser of the primary advertisement and an index value that indicates the advertising effectiveness of the combined advertisement. Specifically, the administrator sets the score so that the score increases as the acceptance of the secondary advertisement is approved and the index value increases, and decreases as the acceptance of the secondary advertisement is rejected. Then, the administrator associates the set score with a combination of information about the primary advertisement and the secondary advertisement, and creates a learning data sample.

[0123] The learning unit 135 uses the created learning data samples to train a model to learn the correspondence between information about the primary advertisement and the secondary advertisement, the approval result for the acceptance of the secondary advertisement by the advertiser of the primary advertisement, and a score based on the advertising effectiveness of the combined advertisement. The learning unit 135 can train the model to learn the above-mentioned correspondence using any network such as a neural network as the learning model. Furthermore, when a neural network is used as the learning model, the learning unit 135 performs a learning process to adjust the values ​​of weights (connection coefficients) that are taken into account when scores (values) are propagated between nodes in each layer that make up the neural network. For example, the learning unit 135 uses any method such as backpropagation (error backpropagation method) to perform a process to optimize (correct) parameters (connection coefficients) so as to reduce the error between the output from the learning model and the correct answer (correct answer data) corresponding to the input. When the learning unit 135 uses the backpropagation technique, it can correct the parameters (connection coefficients) to reduce the error between the output from the learning model and the correct answer (correct answer data) corresponding to the input by performing a learning process to minimize a predetermined loss function.

[0124] Through the above-described learning process, the learning unit 135 can generate an advertising effectiveness estimation model, which is a trained model that receives information about the primary advertisement and the secondary advertisements to be combined and outputs an estimated value of advertising effectiveness obtained by a combined advertisement obtained by combining the primary advertisement and the secondary advertisements. The learning unit 135 registers information about the generated advertising effectiveness estimation model in the advertising effectiveness estimation model storage unit 123.

[0125] Furthermore, the learning unit 135 learns a model that vectorizes each advertisement based on information about the advertisers of each advertisement that constitutes the combined advertisement and the delivery result of the combined advertisement. As a result, the learning unit 135 receives information about the main advertisement and the subordinate advertisements to be combined, and generates an advertisement vector output model that is a trained model that outputs a feature vector obtained by vectorizing the features of the information about the main advertisement and a feature vector obtained by vectorizing the features of the information about the subordinate advertisement.

[0126] For example, the learning unit 135 can train the model so that the similarity between the vectors corresponding to the advertisements that make up the composite advertisement decreases as the size of the advertisers becomes closer. Also, the learning unit 135 can train the model so that the similarity between the vectors corresponding to the advertisements that make up the composite advertisement decreases as the composite advertisement is selected more often.

[0127] The administrator of the information processing device 100 sets information about the advertisers of the primary advertisement and the secondary advertisement, an approval result for the acceptance of the secondary advertisement by the advertiser of the primary advertisement, and a score based on the advertising effectiveness of the combined advertisement.

[0128] The information about the advertisers of the primary and secondary advertisements may be any information related to the advertisements. For example, if the advertiser is a company, the information may be information such as capital and number of employees, and if the advertiser is a listed company, the information may be information about the stock price. Note that the information about the advertisers of the primary and secondary advertisements may be used when setting the score. In this case, the information about the advertisement may be information about the title displayed as text related to the advertisement, a description summarizing the content of the advertisement page (web page) on which the advertisement is displayed, or the content of the advertisement, such as images and videos used as advertising materials.

[0129] The approval result of the primary advertisement's advertiser for accepting the secondary advertisement is a history of information indicating whether the primary advertisement's advertiser approved or denied the acceptance of the secondary advertisement as part of the primary advertisement. The advertising effectiveness of the composite advertisement is determined based on a predetermined indicator of advertising effectiveness. The predetermined indicator of advertising effectiveness can be any indicator, such as cost per acquisition (CPA), cost per click (CPC), click-through rate (CTR), number of conversions, conversion rate (CVR), or page view (PV).

[0130] For example, the administrator of the information processing device 100 pre-sets a score that reflects the result of approval of the acceptance of the secondary advertisement by the advertiser of the primary advertisement and an index value that indicates the advertising effectiveness of the combined advertisement. Specifically, the administrator sets the score so that the score increases as the acceptance of the secondary advertisement is approved and the index value increases, and decreases when the acceptance of the secondary advertisement is rejected. Then, the administrator associates the set score with a combination of information about the advertisers of the primary advertisement and the secondary advertisement, and creates a learning data sample.

[0131] The learning unit 135 uses the created learning data sample to train a model such that the larger the score set for a combination of information about the advertisers of the primary advertisement and the secondary advertisement, the smaller the similarity between the feature vector corresponding to the advertiser of the primary advertisement and the feature vector corresponding to the advertiser of the secondary advertisement is output as a vector. Specifically, the learning unit 135 trains a model that outputs each feature vector such that the larger the set score in the learning data sample, the smaller the cosine similarity between the feature vector corresponding to the primary advertisement and the feature vector corresponding to the secondary advertisement.

[0132] For example, when using an arbitrary network such as a neural network as a training model, the learning unit 135 performs a training process to adjust the values ​​of weights (connection coefficients) that are taken into account when scores (values) are propagated between nodes in each layer of the neural network. For example, the learning unit 135 performs a process to optimize (correct) parameters (connection coefficients) using an arbitrary method such as backpropagation (error backpropagation) so as to reduce the error between the output from the training model and the correct answer (correct answer data) corresponding to the input. In other words, the learning unit 135 performs a process to optimize parameters (connection coefficients) so that the cosine similarity between the feature vector corresponding to the primary advertisement and the feature vector corresponding to the secondary advertisement decreases as the set score in the training data sample increases. Note that when using the backpropagation method, the learning unit 135 can perform a training process to minimize a predetermined loss function, thereby correcting the parameters (connection coefficients) so as to reduce the error between the output from the training model and the correct answer (correct answer data) corresponding to the input.

[0133] Through the above-described learning process, the learning unit 135 can generate an advertisement vector output model, which is a trained model that vectorizes each advertisement, based on information about the advertisers of each advertisement that constitutes the composite advertisement and the delivery result of the composite advertisement. The learning unit 135 registers information about the generated advertisement vector output model in the advertisement vector output model storage unit 124.

[0134] [3. Processing Procedure] (3-1. Synthetic Ad Delivery Procedure (Part 1)) Hereinafter, an example of an information processing procedure executed by the information processing device 100 according to the embodiment will be described with reference to the drawings. Hereinafter, an example of a procedure (part 1) for delivering a composite advertisement according to the embodiment will be described with reference to FIG. 10. FIG. 10 is a flowchart showing an example of a procedure (part 1) for delivering a composite advertisement according to the embodiment. The processing procedure shown in FIG. 10 is executed by the control unit 130 of the information processing device 100. The processing procedure shown in FIG. 10 is repeatedly executed while the information processing device 100 is operating.

[0135] As shown in FIG. 10, the selection unit 131 selects a primary advertisement and a secondary advertisement from among a plurality of advertisements to be combined (step S101).

[0136] Furthermore, the generating unit 132 generates a combined advertisement by combining a part of the main advertisement with the secondary advertisement, based on the main advertisement and the secondary advertisement selected by the selecting unit 131 (Step S102).

[0137] Furthermore, the distribution unit 133 distributes the combined advertisement generated by the generation unit 132 through the communication unit 110 (step S103), and the processing procedure shown in FIG. 10 ends.

[0138] (3-2. Synthetic Ad Delivery Procedure (Part 2)) Hereinafter, an example of a procedure (part 2) for delivering a composite advertisement according to the embodiment will be described with reference to Fig. 11. Fig. 11 is a flowchart showing an example of a procedure (part 2) for delivering a composite advertisement 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.

[0139] As shown in FIG. 11, the selection unit 131 selects a main advertisement and a subordinate advertisement from among a plurality of advertisements to be combined, based on the distribution result of the combined advertisement, in accordance with a selection rule that is defined in advance (step S201).

[0140] Furthermore, the generating unit 132 generates a combined advertisement by combining a part of the main advertisement with the secondary advertisement, based on the main advertisement and the secondary advertisement selected by the selecting unit 131 (Step S202).

[0141] Furthermore, the distribution unit 133 distributes the combined advertisement generated by the generation unit 132 through the communication unit 110 (step S203), and the processing procedure shown in FIG. 11 ends.

[0142] (3-3. Synthetic Ad Delivery Procedure (Part 3)) Hereinafter, an example of a procedure (part 3) for delivering a composite advertisement according to the embodiment will be described with reference to Fig. 12. Fig. 12 is a flowchart showing an example of a procedure (part 3) for delivering a composite advertisement according to the embodiment. The processing procedure shown in Fig. 12 is executed by the control unit 130 of the information processing device 100. The processing procedure shown in Fig. 12 is repeatedly executed while the information processing device 100 is operating.

[0143] As shown in FIG. 12, the selection unit 131 selects a plurality of advertisements for conveying information about an advertiser different from the other advertisers from among the advertisements submitted by the other advertisers (step S301).

[0144] Next, the distribution unit 133 distributes the combined advertisement generated by combining the selected advertisements via the communication unit 110 (step S302), and the processing procedure shown in FIG. 12 ends.

[0145] [4. 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.

[0146] (4-1. Display of synthetic advertisements) In the above embodiment, the information processing device 100 may separately count user responses, such as the number of clicks on the composite advertisement, for the primary advertisement and the secondary advertisements that constitute the composite advertisement, and dynamically change the display mode of the composite advertisement based on the counting results and deliver the composite advertisement. For example, the information processing device 100 may dynamically change the display size of an image, the saturation of the display color of the image, the transparency of the image, etc., depending on the cumulative number of clicks. Specifically, the information processing device 100 may increase the display size, darken the display color, or reduce the transparency of the advertisement that has a larger cumulative number of clicks, out of the primary advertisement and the secondary advertisement.

[0147] Furthermore, in the above embodiment, the information processing device 100 may change the subordinate advertisements to advertisements that are different in time series, without changing the main advertisement, among the advertisements that make up the combined advertisement.

[0148] (4-2. Bidding for secondary advertisements) In the above embodiment, the information processing device 100 may receive a selection of a main advertisement desired to be parasitized from an advertiser of a secondary advertisement. Then, if there is an overlap in the requests, the information processing device 100 may execute a bidding to win the right to parasitize.

[0149] (4-3. Remuneration to the main advertiser) In the above embodiment, the information processing device 100 may kick back a predetermined reward to the advertiser of the main advertisement when the secondary advertisements constituting the composite advertisement achieve a predetermined result.

[0150] [5. 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. 13. Fig. 13 is a hardware configuration diagram showing an example of a computer that realizes the functions of the information processing device according to the embodiment.

[0151] 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.

[0152] 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.

[0153] 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.

[0154] 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.

[0155] 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.

[0156] 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.

[0157] 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.

[0158] [6. 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.

[0159] 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.

[0160] Furthermore, the above-described embodiments can be combined as appropriate within the scope of not causing any contradiction in the processing content.

[0161] [7. Effects] The information processing device 100 according to the embodiment includes a selection unit 131 and a distribution unit 133. The selection unit 131 selects a plurality of advertisements from advertisements submitted by advertisers other than the requesting advertiser as materials for generating a composite advertisement for transmitting information about the requesting advertiser requesting generation of the composite advertisement. The distribution unit 133 distributes the composite advertisement generated by combining the advertisements selected by the selection unit 131.

[0162] Furthermore, in the information processing device 100 according to the embodiment, the selection unit 131 identifies a related category that is associated with the category corresponding to the requesting advertiser based on the correspondence relationship of categories (for example, industry) that is preset for each advertiser, and selects the advertisements to be combined from among the advertisements input by the advertisers that are linked to the identified related category.

[0163] In addition, in the information processing device 100 according to the embodiment, when multiple related categories are identified, the selection unit 131 selects advertisements to be combined from each of the advertisements submitted by advertisers linked to each of the identified related categories.

[0164] For this reason, the information processing device 100 according to the embodiment can realize new advertisement delivery without being bound by existing methods of advertisement delivery, by using the processes executed by the above-mentioned units or any combination of the units.

[0165] 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.

[0166] 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]

[0167] 10 User terminal 20 Advertiser Terminal 100 Information processing device 110 Communications Department 120 Storage section 121 Advertising information storage unit 122 Distribution result information storage unit 123 Advertising effectiveness estimation model memory unit 124 Advertising vector output model memory unit 125 Industry-specific information storage unit 130 control section 131 Selection Section 132 Generation part 133 Distribution Department 134 Decision Section 135 Learning Department

Claims

1. a selection unit that selects a plurality of advertisements from advertisements submitted by advertisers other than the requesting advertiser as materials for generating a composite advertisement for transmitting information about the requesting advertiser requesting generation of the composite advertisement; a distribution unit that distributes a composite advertisement generated by combining the advertisements selected by the selection unit; and The selection unit Based on the correspondence relationship between the types of business conducted by the advertisers, a related business type associated with the business type corresponding to the requesting advertiser is identified, and the advertisement to be combined is selected from advertisements input by advertisers linked to the identified related business type.

1. An information processing device comprising:

2. The selection unit When a plurality of related industries are identified, an advertisement to be combined is selected from each of the advertisements submitted by advertisers associated with each of the identified related industries.

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

3. 1. A computer-implemented information processing method, comprising: a selection step of selecting a plurality of advertisements from advertisements submitted by advertisers other than the requesting advertiser as materials for generating a composite advertisement for transmitting information about the requesting advertiser requesting the generation of the composite advertisement; a distribution step of distributing a composite advertisement generated by combining the advertisements selected in the selection step; Including, The selection step includes: Based on the correspondence relationship between the types of business conducted by the advertisers, a related business type associated with the business type corresponding to the requesting advertiser is identified, and the advertisement to be combined is selected from advertisements input by advertisers linked to the identified related business type. An information processing method comprising:

4. On the computer, a selection step of selecting a plurality of advertisements from advertisements submitted by advertisers other than the requesting advertiser as materials for generating a composite advertisement for transmitting information about the requesting advertiser requesting the generation of the composite advertisement; a distribution step of distributing a composite advertisement generated by combining the advertisements selected by the selection step; Execute The selection procedure comprises: Based on the correspondence relationship between the types of business conducted by the advertisers, a related business type associated with the business type corresponding to the requesting advertiser is identified, and the advertisement to be combined is selected from advertisements input by advertisers linked to the identified related business type. An information processing program characterized by:

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