Information processing apparatus

Abstract

To improve the accuracy of narrowing down target users of advertisement distribution.SOLUTION: An information processing apparatus 1 comprises a storage unit 14 that stores a program and data on a persona of each of SNS users to whom SNS services are to be provided, a processor 11 that executes the program, and a communication unit 17 that communicates through an Internet circuit 8 with an SNS server 2 to provide SNS services. The processor functions, by executing the program, as means for extracting a first SNS user having a persona identical to or similar to a particular one of the SNS users from the SNS users (S32), means for requesting an account of a second SNS user as a follower to the first SNS user to the SNS server through the communication unit and downloading it (S33), and means for generating a list of purchase candidates of a particular commodity the particular SNS user has posted from the second SNS user (S34, S35).SELECTED DRAWING: Figure 11

Description

[Technical field]

[0001] An embodiment of the present invention relates to an information processing device. [Background technology]

[0002] Recently, it has become difficult to continue selling products in a store while sales staff are serving customers (physical store sales). The ratio of physical store sales to total sales is decreasing, and the ratio of sales using electronic commerce (EC) via the Internet (EC sales) is increasing.

[0003] Furthermore, a sales model that utilizes SNS (Social Networking Service) to attract customers to EC sites, in which specific SNS users (image posters), such as sales staff at physical stores, post photos (images) of products on Instagram (registered trademark), for example, and then direct SNS users from those posts to the EC site, leading to product sales, is attracting attention.

[0004] This sales model using SNS has shifted from a so-called "passive sales" where SNS users wait for an opportunity to visit the poster's posting page to a proactive sales activity where advertisements are sent to individual SNS users to guide them to the posting page. There are various types of advertisements, such as display advertisements and email advertisements. In either case, advertisements are sent to a narrow range of target users, but the success rates, such as click rates and conversion rates, are not very high.

[0005] One of the reasons for this is thought to be that the accuracy of narrowing down the target users for ad delivery is not very high. Summary of the Invention [Problem to be solved by the invention]

[0006] The aim is to improve the accuracy of narrowing down the target users for ad delivery. [Means for solving the problem]

[0007] The information processing device according to the present embodiment includes a storage unit that stores a program, data on the personas of each SNS user who receives the SNS service, a processor that executes the program, and a communication unit that communicates with an SNS server that provides the SNS service via an Internet line. By executing the program, the processor functions as a means for extracting a first SNS user who has a persona identical or similar to a specific SNS user from among the SNS users, a means for requesting and downloading an account of a second SNS user who is a follower of the first SNS user from the SNS server via the communication unit, and a means for generating a list of purchase candidates for a specific product posted by the specific SNS user from the second SNS users. [Brief description of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram showing an example of a screen configuration of an SNS service. [Diagram 2] FIG. 2 is a configuration diagram of an entire system including the information processing device according to this embodiment. [Diagram 3] FIG. 3 is a configuration diagram of the information processing device of FIG. [Figure 4] FIG. 4 is a diagram illustrating an example of a persona. [Diagram 5] FIG. 5 is a diagram illustrating an example of a classification system of objects of interest identified by the processor of FIG. [Figure 6] FIG. 6 is a flowchart showing the procedure of the persona creation process implemented by the processor of FIG. [Figure 7] FIG. 7 is a conceptual diagram of the persona creation process of FIG. [Figure 8] FIG. 8 is a flowchart showing a typical procedure for creating a persona of an SNS user who has a strong relationship with user A, which is realized by the processor of FIG. [Figure 9] FIG. 9 is a conceptual diagram of the exemplary persona creation process of FIG. [Figure 10]FIG. 10 is a flowchart showing the procedure of the purchase candidate extraction process (human axis) realized by the processor of FIG. [Figure 11] FIG. 11 is a conceptual diagram of the extraction of potential purchasers in FIG. [Figure 12] FIG. 12 is a flowchart showing the procedure of the candidate purchaser extraction process (product axis 1) realized by the processor of FIG. [Figure 13] FIG. 13 is a conceptual diagram of the extraction of potential purchasers in FIG. [Figure 14] FIG. 14 is a flowchart showing the procedure of the candidate purchaser extraction process (product axis 2) realized by the processor of FIG. [Figure 15] FIG. 15 is a conceptual diagram of the extraction of potential purchasers in FIG. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0009] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. This embodiment relates to a process of extracting product purchase candidates by utilizing image (still image (photo), video) posting and viewing services such as "Facebook (registered trademark)", "Twitter (registered trademark)", and "Instagram (registered trademark)" in social networking services (hereinafter referred to as "SNS") as community-type services that promote and support connections between people. As shown in FIG. 1 as an example of a screen of an image posting and viewing service, an SNS user who receives the SNS service is provided with a function to register a profile and to post photos and videos (hereinafter referred to as images) related to hobbies, products for sale, etc., together with his / her own comments. On the other hand, an SNS user who views posted images, etc. is provided with a function to react to the user who posted the image (image poster) and the post and to perform some kind of action. For example, various functions are provided, such as a function to follow the image poster and grasp the update status of the image poster, a function to send a comment on the post, a function to express sympathy such as "like" to the post, a function to retweet and share to recommend and spread the post to one's followers, etc., and a function to save (download) the posted image.

[0010] It should be noted that SNS users who respond to an image poster or its post by executing actions such as the follow function, comment posting function, sympathetic communication function such as "like", diffusion function such as retweeting and sharing, and save function, and perform some action, are considered to be SNS users who find the target image poster or its post attractive and show a strong interest. Here, SNS users who execute at least one of the functions of the follow function, comment posting function, sympathetic communication function such as "like", diffusion function such as retweeting and sharing, and save function are said to be SNS users who show an interest in the image poster or its post.

[0011] 2, an SNS server 2 that provides an SNS service is connected to an information processing device 1 according to this embodiment via a public communication line network (Internet line) 8. In addition, terminals (user terminals) 3-7, such as PCs, smartphones, PHSs, PDAs, tablet terminals, etc. owned by SNS users such as image contributors and viewers, are connected to the SNS server 2 via the Internet line 8.

[0012] For example, an SNS user acting as an image poster can post images, comments, etc. to the SNS server 2 via a user terminal 3. On the other hand, an SNS user acting as a viewer can transmit various reactions to a post via a user terminal 4-7, such as comments on the post, sympathy for the post such as "likes," antipathy for the post such as "no," spreading the post by retweeting or sharing, saving (downloading) the posted image, and following the poster.

[0013] 3, in the information processing device 1, a RAM 12, a ROM 13, a storage unit 14, an input controller 15, a video controller 16, and a communication unit 17 are connected to a processor 11 via a data / control bus 10. An input device 18 such as a keyboard or a mouse is connected to the input controller 15. A display 19 such as an LCD (Liquid Crystal Display) is connected to the video controller 16.

[0014] The processor 11 is composed of a CPU (Central Processing Unit) and a GPU (Graphics Processing Unit). The processor 11 executes various programs loaded from the storage unit 14 or ROM 13 to the RAM 12 to execute a persona creation process, a typical persona creation process, and three types of purchase candidate extraction processes, which will be described later. The RAM 12 functions as the main memory, work area, etc. of the processor 11. The ROM 13 or the storage unit 14 stores a BIOS (Basic Input Output System) executed by the processor 11, an operating system program (OS), a persona creation program according to this embodiment, a typical persona creation program, three types of purchase candidate extraction programs, other programs for realizing various functions such as preprocessing, and various data required for these processes.

[0015] Next, the persona creation process will be described. As shown in Fig. 4(a) to Fig. 4(c), a persona is defined as a profile of an SNS user, and is set here from each item related to product purchasing behavior, such as age, sex, place of residence, interests, and trends that indicate recent interests. As shown in Fig. 5, interests, which are the items most related to product purchasing behavior, are more specifically composed of major categories such as fashion and automobiles, and lower-level medium and small categories.

[0016] Various means relating to the persona creation processing are realized by the processor 11 executing a persona creation program loaded from the storage unit 14 or the ROM 13 to the RAM 12 .

[0017] 6 and 7, first, in step S11, a request is made to the SNS server 2 via the communication unit 17 for downloading the profiles registered by each SNS user, the images posted by each SNS user, the hashtags added to the posted images, and the comments sent by each SNS user on their own posts. The profile data separated by the separation means 302 is passed to the persona creation means 307, the posted image data is passed to the object extraction means 303, and the hashtags and comments are passed to the language analysis means 305.

[0018] In step S12, the language analysis means 305 performs language analysis processing (text mining) on ​​the downloaded hashtags and comments to extract nouns, and the interest identification means 306 identifies the major category of the SNS user's interests based on the extracted nouns. The identification processing may involve building a correspondence table of many nouns and multiple major categories in advance, consulting the correspondence table individually for each noun, and identifying the major category of interests showing the maximum frequency as the major category of the SNS user's interests, or may involve inputting multiple nouns using a trained neural network and outputting the major category of interests.

[0019] In step S13, the object extraction means 303 uses the identified major category of the interest to extract a partial image including an object corresponding to the major category of the interest from the posted image, for example, a jacket corresponding to fashion. This extraction process typically uses a convolutional neural network. Since the scope of the image analysis process described below can be limited to partial images including an object corresponding to the major category of the interest, it is expected that the accuracy of the image analysis can be improved.

[0020] The image analysis means 304 is divided into a plurality of discrimination means 304-1, 304-2, ... 304-n optimized for each major classification of objects of interest. The plurality of discrimination means 304-1, 304-2, ... 304-n are typically configured by a convolutional neural network system. As is well known, a convolutional neural network extracts features from a two-dimensional image by performing convolution using a filter. The convolutional neural network is made up of a multi-layer network that repeats convolution and pooling. In the convolutional neural network, the coefficients (weights) of the filters that are effective for discrimination and that constitute the convolutional layer are trained using a large amount of data such as a large amount of learning images. The coefficients (weights) are obtained by performing training that acquires invariance against various deformations by repeating convolution using a filter and pooling that summarizes responses in a certain area using a large amount of data. The discrimination performance of the convolutional neural network depends on the filters that constitute the convolutional layer. A filter is prepared for each major classification of objects of interest.

[0021] In step S14, one of the multiple classification means 304-1, 304-2, ... 304-n that matches the identified major classification of the subject of interest performs image analysis processing on the partial image to estimate a lower level medium classification and small classification of the subject of interest. If the subject of interest (major classification) is "fashion", a classification means constructed to match the major classification "fashion" is selected, and the selected classifier analyzes the partial image and outputs "cool" as the medium classification and "high brand" as the small classification (see FIG. 4(a)).

[0022] In addition, when there are multiple posted images posted by the SNS user, steps S12, S13, and S14 are repeated for each posted image. Since a major category, a medium category, and a small category of interests are identified for each posted image, there may be multiple major categories, medium categories, and small categories. In this case, the major category, medium category, and small category with the highest frequency are identified as the major category, medium category, and small category of representative interests for the SNS user.

[0023] In step S15, the trend analysis means 308 identifies trends that indicate matters in which the SNS user has recently shown interest, based on the results of language analysis of the hashtags and comments attached to the latest or a predetermined number of images posted by the SNS user. For example, the SNS user extracts multiple nouns by language analysis processing from the hashtags attached to the latest or a predetermined number of images posted by the SNS user and the user's comments, tallies the frequency of appearance of each noun, and identifies the noun that appears most frequently as the trend. For example, when the frequency of the noun "Korean fashion" is the highest, "Korean fashion" is identified as the trend for the user (see FIG. 4(a)).

[0024] In step S16, the persona creation means 307 creates persona data by associating specific items in the profile, such as age, sex, place of residence, interests (major categories, medium categories, minor categories), and trends, with the account of the SNS user. The persona data is stored in the storage unit 14 in step S17.

[0025] Steps S11-S17 are repeated through step S18, and persona data is created and stored for all SNS users who receive the SNS service or for SNS users included in a specific group that is set in advance.

[0026] 8 and 9, a process for creating a typical persona of an SNS user (D) who has a strong interest in a specific user A and also in products posted by user A. It is assumed here that user A is a salesperson working at an apparel shop, an SNS user, and an image contributor who posts images of various products sold at the apparel shop.

[0027] In step S21, the account of user A or user B who has a strong interest in the post is requested and downloaded from the SNS server 2 via the communication unit 17. Examples of user A or user B who has a strong interest in the post include followers of user A, users who have expressed sympathy for user A's post by "liking" or the like, users who have spread user A's post by retweeting or sharing, users who have saved (downloaded) images posted by user A, users who have posted comments on user A's post, and the like.

[0028] In step S22, the hash tag attached to the posted image of user A is requested from the SNS server 2 via the communication unit 17, and downloaded.

[0029] In step S23, the name (product name) of the product posted by user A is identified based on the language analysis processing result of the hashtag attached to the posted image of user A. Note that the name (product name) of the product posted by user A may be input by the operator via the input device 18.

[0030] In step S24, the account of user C who posted an image with a hashtag including the name of the product (product name) posted by user A is requested and downloaded from the SNS server 2 via the communication unit 17. Since user C posted an image of the same product as the product posted by user A, it is presumed that user C is showing a strong interest in the product posted by user A.

[0031] In step S25, multiple users D are extracted that overlap with user B who shows a strong interest in user A or his / her posts and user C who shows a strong interest in the product posted by user A. It can be said that user D is a user who shows a strong interest in both user A or his / her posts and the product posted by user A.

[0032] In step S26, the persona data of user D is read from the storage unit 14, and in step S27, the persona of user D is integrated to create a typical persona (PA) of user D. The items constituting the persona are tallied by age group, sex, place of residence, interest (major category), interest (medium category), interest (minor category), and trend, and the specific contents of the age group, sex, place of residence, interest (major category), interest (medium category), interest (minor category), and trend that show the highest frequency are identified. A typical persona can be said to be a group of elements that characterize the personality of multiple users D who show a strong interest in user A or his posts and also show a strong interest in products posted by user A.

[0033] In step S28, the exemplary persona data is stored in the storage unit 14 in association with User A's account.

[0034] Next, the process of extracting potential purchasers will be described. It is assumed that the process of using an SNS service to purchase a product is as follows: a user becomes very interested in User A or his / her posts, and while viewing User A's posts many times, he / she becomes very interested in the product posted by User A, and moves to an EC site via User A's post and purchases the product sold on the EC site.

[0035] In this embodiment, two methods are provided for extracting users who are likely to show strong interest in user A or his / her posts in the future as purchase candidates: a method for extracting purchase candidates mainly starting from the persona of a specific user A (referred to as a person-based method), and a method for extracting purchase candidates starting from products posted by user A (referred to as a product-based method). Furthermore, two types of the latter product-based method are provided. In this embodiment, the person-based method and the two types of product-based methods may be equipped and applied selectively, or the purchase candidates extracted by all or any two of the person-based method and the two types of product-based methods may be further narrowed down by the logical product of the two methods.

[0036] 10 and 11 show the processing procedure of the man-axis method. In step S31, the persona data of the SNS user created in advance from the storage unit 14, that is, the persona data of each of the specific user A and other users, is read out, and in step S32, a user E having the same or similar persona as user A is extracted from the other users as a user with a similar personality to user A. User E is the same as user A in all items including age, sex, place of residence, interests (major categories), interests (medium categories), interests (minor categories), and trends. Alternatively, user E refers to a case where some items of age, sex, place of residence, interests (major categories), interests (medium categories), interests (minor categories), and trends are different from user A, and other items are the same. Here, as the different items, items other than the items considered to be important for showing strong interest in people are set in advance, and for example, place of residence and interests (minor categories) are set. In other words, users whose place of residence and interests (minor categories) are different from those of user A, but whose other categories such as age, gender, interests (major categories), interests (medium categories), and trends are the same as those of user A, are extracted.

[0037] In step S33, the account of user E or user F who shows a strong interest in the post is requested from the SNS server 2 via the communication unit 17 and downloaded. That is, it is assumed that user F who shows a strong interest in user E who has a similar personality to specific user A is a user who is highly likely to show a strong interest in specific user A as well. As described above, users F who show a strong interest in user E or the post include followers of user E, users who have expressed sympathy for user E's post by "Like" or the like, users who have spread user E's post by retweeting or sharing, users who have saved (downloaded) images posted by user E, users who have posted comments on user E's post, etc.

[0038] In step S34, user G is extracted from user F by excluding user D who has already shown a strong interest in user A or his posts and also in the products posted by user A. User G is assumed to be a user who has shown a strong interest in user E, whose personality is similar to user A, but who has not had any contact with user A up until now.

[0039] In step S35, data of a typical persona (PA) of user D who shows a strong interest in user A or the posts made by user A and also in the products posted by user A, created in advance, is read from the storage unit 14, and user H who has a persona identical or similar to the typical persona (PA) is extracted from user G. Here, as described above, "similar" refers to a case where some items among age group, sex, place of residence, interests (major categories), interests (medium categories), interests (minor categories), and trends are different and other items are the same, and items other than those considered important for showing a strong interest in a person are set in advance as different items, for example, place of residence and interests (minor categories) are set.

[0040] It is assumed that User H is highly likely to show a strong interest in User A and purchase products posted by User A in the future. In other words, User H is highly likely to become a follower of User A or to respond to User A's posts by expressing sympathy, such as by clicking "Like," and is highly likely to move to an EC site via User A's post and ultimately purchase a product.

[0041] 12 and 13 show the processing steps of the first product axis method. First, in step S41, a hashtag attached to an image posted by a specific user A is requested from the SNS server 2 via the communication unit 17 and downloaded. In step S42, the names (product names) of the products a, b, and c posted by user A are identified based on the results of language analysis processing of the hashtag. The names (product names) of the products posted by user A may be input by the operator via the input device 18. In step S43, the names of similar products d and e that are similar to the products a, b, and c posted by user A are input by the operator via the input device 18. Typically, the names of products competing with the products a, b, and c posted by user A are input as the similar products d and e.

[0042] In step S44, the account of user J who posted an image with a hashtag including the names of products a, b, and c posted by user A is requested from the SNS server 2 via the communication unit 17, and downloaded. Similarly, in step S45, the account of user K who posted an image with a hashtag including the names of similar products d and e similar to the products posted by user A is requested from the SNS server 2 via the communication unit 17, and downloaded. Since users J and K have posted products a, b, and c or similar products d and e posted by user A, it is assumed that they have already purchased products a, b, c, d, and e, or have a strong interest in them even if they have not purchased them, and that the products handled by the apparel shop where user A works are relatively consistent with the user's preferences.

[0043] In step S46, user D, who has already shown a strong interest in user A or his posts, is excluded from users J and K, and it is assumed that the users are those who have shown a strong interest in the products posted by user A or similar products, but who have not had any contact with user A until now.

[0044] In step S47, data of a typical persona (PA) of user D who shows a strong interest in user A or the post made by the user A and in the posted product or a similar product, created in advance, is read from the storage unit 14, and user M who has a persona identical to or similar to the typical persona (PA) is extracted from user L. Here, "similar" is as described above.

[0045] User L is assumed to be a potential buyer who is highly likely to become interested in products posted by a specific user A in the future, and who is likely to become interested in products posted by a specific user A, move to an EC site via user A's post, and ultimately purchase the product.

[0046] 14 and 15 show the processing steps of the second product axis method. First, in step S51, the hashtag attached to the image posted by a specific user A is requested and downloaded from the SNS server 2 via the communication unit 17. In step S52, the names (product names) of the products a, b, and c posted by user A are identified based on the results of language analysis processing of the hashtag. Of course, the names (product names) of the products posted by user A may be input by the operator via the input device 18.

[0047] In step S53, the account of user J who posted the image with the hashtag including the names of products a, b, and c posted by user A is requested from the SNS server 2 via the communication unit 17, and is downloaded.

[0048] In step S54, user N is extracted from user J by excluding user A downloaded in step S21 or user B who shows a strong interest in the posted product of user A. It is assumed that user N is a user who shows a strong interest in the product posted by user A, but has not had any contact with user A up until now.

[0049] In step S55, data of a typical persona (PA) of user A or user D who shows a strong interest in the posts of user A, which was created in advance, is read from the storage unit 14, and user P having a persona identical to or similar to the typical persona (PA) is extracted from user N. Here, "similar" is as described above.

[0050] User P is assumed to be a potential buyer who is highly likely to become interested in products posted by a specific user A in the future, and who is likely to become interested in products posted by a specific user A, move to an EC site via user A's post, and ultimately purchase the product.

[0051] According to this embodiment, it is possible to extract potential purchasers who are highly likely to show a strong interest in a specific user A in the future, move to an EC site via user A's posts, and ultimately purchase a product, or potential purchasers who are highly likely to show a strong interest in products posted by user A in the future, move to an EC site via user A's posts, and ultimately purchase a product. Therefore, it is possible to realize effective advertisement delivery to these potential purchasers.

[0052] Although some embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included in the scope of the invention and its equivalents described in the claims, as well as in the scope and spirit of the invention. [Explanation of symbols]

[0053] 1...information processing device, 2...SNS server, 3-7...user terminal.

Claims

1. A storage unit that stores the program and data of the persona of each SNS user who receives the SNS service; A processor for executing the program; An information processing device including an SNS server that provides the SNS service and a communication unit that communicates with the SNS server via an Internet line, The processor executes the program, A means for extracting a first SNS user having a persona identical to or similar to a specific SNS user from the SNS users; a means for requesting and downloading an account of a second SNS user who is a follower of the first SNS user from the SNS server via the communication unit; An information processing device that functions as a means for generating a purchase candidate list of a specific product posted by the specific SNS user from the second SNS user.

2. A storage unit that stores the program and data of the persona of each SNS user who receives the SNS service; A processor for executing the program; An information processing device including an SNS server that provides the SNS service and a communication unit that communicates with the SNS server via an Internet line, The processor executes the program, a means for requesting, via the communication unit, an account of a second SNS user who has posted about a specific product posted by a specific SNS user among the SNS users, and downloading the account; a means for requesting, via the communication unit, an account of a third SNS user who has posted a product similar to the specific product from the SNS user, and downloading the account; An information processing device that functions as a means for generating a list of purchase candidates for the specific product from the second and third SNS users.

3. A storage unit that stores the program and data of the persona of each SNS user who receives the SNS service; A processor for executing the program; An information processing device including an SNS server that provides the SNS service and a communication unit that communicates with the SNS server via an Internet line, The processor executes the program, a means for requesting and downloading an account of a second SNS user who is a follower of a specific SNS user among the SNS users from the SNS server via the communication unit; a means for requesting, via the communication unit, an account of a third SNS user who has posted about a specific product posted by the specific SNS user, and downloading the account; a means for excluding the second SNS users from the third SNS users and extracting a fourth SNS user; An information processing device that functions as a means for generating a list of purchase candidates for the specific product from the fourth SNS users.

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