Information processing apparatus

Abstract

To provide an information processing apparatus which improves accuracy in narrowing target users for advertisement distribution.SOLUTION: An information processing 1 according to the present invention has a storage unit 14 for storing a program and persona data of each of SNS users receiving a provided SNS service, a processor 11 for executing the program, and a communication unit 17 for communicating through an internet circuit 8 with a SNS server 2 for providing a SNS service. The processor executes the program, thereby functioning as means S44 for requesting, to the SNS server through the communication unit, an account of a second SNS user who posted as to a specific article posted by a specific SNS user among SNS users to download it, means S45 for requesting, to the SNS server through the communication unit, an account of a third SNS user who posted as to an article similar to the specific article to download it, and means S46, S47 for extracting a candidate for purchasing the specific article from the second and third SNS users.SELECTED DRAWING: Figure 12

Description

[Technical Field]

[0001] FIELD 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 stores where sales staff are on hand to serve customers (brick-and-mortar sales). The proportion of brick-and-mortar sales in total sales is declining, and the proportion 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 e-commerce sites is gaining attention. In other words, specific SNS users (image posters), such as sales staff at physical stores, post photos (images) of products on Instagram (registered trademark), and then direct SNS users to the e-commerce site from those posts, thereby leading to product sales.

[0004] This sales model using SNS has shifted from so-called "passive sales" where SNS users wait for an opportunity to visit the poster's posting page, to proactive sales activities where advertisements are sent individually to SNS users to direct them to the posting page. There are various types of advertisements, such as display advertisements and email advertisements. All of these advertisements are sent to a narrow target audience, 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 advertisement delivery is not very high. Summary of the Invention [Problem to be solved by the invention]

[0006] The purpose 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 this embodiment includes a storage unit that stores a program and 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 connection. By executing the program, the processor functions as a means for requesting and downloading from the SNS server, via the communication unit, the account of a second SNS user who posted about a specific product posted by a specific SNS user among the SNS users, a means for requesting and downloading from the SNS server, via the communication unit, the account of a third SNS user who posted about a product similar to the specific product, and a means for extracting potential purchasers of the specific product from the second and third SNS users. [Brief explanation of the drawings]

[0008] [Figure 1] FIG. 1 is a diagram showing an example of the screen configuration of an SNS service. [Figure 2] FIG. 2 is a configuration diagram of an entire system including an information processing device according to this embodiment. [Figure 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. [Figure 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 typical 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 shown 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 candidate purchasers shown 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 candidate purchasers shown in FIG. DETAILED DESCRIPTION OF THE INVENTION

[0009] Hereinafter, an embodiment of the present invention will be described with reference to the drawings. This embodiment relates to a process for extracting product purchase candidates by utilizing a social networking service (hereinafter referred to as "SNS"), such as Facebook (registered trademark), Twitter (registered trademark), or Instagram (registered trademark), which is a community-based service that promotes and supports connections between people. As shown in FIG. 1 , an example of a screen of the image posting and viewing service is shown. SNS users who use the SNS service register a profile and are provided with a function for posting photos and videos (hereinafter referred to as "images") related to hobbies, products, etc., along with their own comments. Meanwhile, SNS users who view posted images are provided with a function for reacting to the user who posted the image (image poster) and the post and performing some kind of action. For example, various functions are provided, such as a function for following the image poster to keep track of the image poster's updates, a function for sending comments on posts, a function for expressing sympathy by "liking" a post, a function for recommending and spreading a post to their followers, such as by retweeting or sharing, and a function for saving (downloading) posted images.

[0010] SNS users who respond to an image poster or their post by performing actions such as the follow function, comment posting function, sympathetic communication function such as "like," diffusion function such as retweet or share, and save function, and take some kind of action, are considered to be SNS users who find the target image poster or their post attractive and show a strong interest. Here, SNS users who perform at least one of the functions of the follow function, comment posting function, sympathetic communication function such as "like," diffusion function such as retweet or share, and save function are said to be SNS users who show an interest in the image poster or their post.

[0011] 2, an SNS server 2 that provides SNS services is connected to an information processing device 1 according to this embodiment via a public communication network (Internet line) 8. In addition, terminals (user terminals) 3-7, such as PCs, smartphones, PHSs, PDAs, and tablet terminals 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 send various reactions via user terminals 4-7, such as comments on the post, expressing sympathy for the post by "liking" the post, expressing dislike for the post by "not liking" the post, 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 the ROM 13 into 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), the persona creation program according to this embodiment, the typical persona creation program, the three types of purchase candidate extraction program, other programs for realizing various functions such as preprocessing, and various data required for these processes.

[0015] Next, the persona creation process will be explained. As shown in Figures 4(a) to 4(c), a persona is defined as a profile of an SNS user, and in this case, it is set from various items related to product purchasing behavior, such as age, gender, place of residence, interests, and trends that represent recent interests. Furthermore, as shown in Figure 5, interests, which are the items most related to product purchasing behavior, are more specifically composed of major categories such as fashion and automobiles, as well as lower-level medium and small categories.

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

[0017] 6 and 7, first, in step S11, the profiles registered by each SNS user, the images posted by each SNS user, the hashtags attached to the posted images, and the comments posted by each SNS user on their own posts are requested and downloaded from the SNS server 2 via the communication unit 17. 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 categories of the SNS user's interests based on the extracted nouns. This identification processing may involve building a correspondence table of many nouns and multiple major categories in advance, individually querying the correspondence table for nouns, and identifying the major category of the interest that shows the greatest frequency as the major category of the SNS user's interests, or it may involve inputting multiple nouns using a trained neural network and outputting the major categories of the interests.

[0019] In step S13, the object extraction means 303 uses the identified major category of the subject of interest to extract from the posted image a partial image containing an object corresponding to the major category of the subject of interest, 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 containing an object corresponding to the major category of the subject of interest, improved image analysis accuracy can be expected.

[0020] The image analysis means 304 is divided into multiple classification means 304-1, 304-2, ..., 304-n, each optimized for a specific major classification of the object of interest. The multiple classification means 304-1, 304-2, ..., 304-n are typically configured as a convolutional neural network system. As is well known, a convolutional neural network extracts features from a two-dimensional image by convolving a filter on the image. A convolutional neural network is a multilayer network that repeats convolution and pooling. The coefficients (weights) of the filters that constitute the convolutional layer of the convolutional neural network, which are effective for classification, are trained using a large amount of data, such as a large number of training images. The coefficients (weights) are obtained by learning to acquire invariance to various deformations by repeating convolution using a filter and pooling that summarizes responses in a certain region using a large amount of data. The classification performance of a convolutional neural network depends on the filters that constitute the convolutional layer. A filter is prepared for each major classification of the object of interest.

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

[0022] If there are multiple images posted by the SNS user, steps S12, S13, and S14 are repeated for each posted image. Since a major category, medium category, and 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 represent topics that the SNS user has recently shown interest in 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 trend analysis means 308 extracts multiple nouns from the hashtags attached to the latest or a predetermined number of images posted by the SNS user through language analysis processing, and the user's comments, and tallies the frequency of appearance of each noun, and identifies the noun with the highest frequency as the trend. For example, if the frequency of the noun "Korean fashion" is the highest, "Korean fashion" is identified as the trend for that 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, gender, 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 relating to all SNS users who receive the SNS service or SNS users included in a specific group set in advance is created and stored.

[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 the products posted by user A. Here, it is assumed that user A is a salesperson working at an apparel shop, an SNS user, and an image poster 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 shown 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 shown 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, and users who have posted comments on user A's post.

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

[0029] In step S23, the name of the product (product name) posted by user A is identified based on the results of language analysis processing of the hashtag attached to the image posted by user A. Note that the name of the product (product name) 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 from the SNS server 2 via the communication unit 17 and downloaded. 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 products posted by user A. User D can be said to be a user who shows a strong interest in both user A or his / her posts and the products 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 that make up the persona are tallied by age group, gender, place of residence, interest (major category), interest (medium category), interest (minor category), and trend, and the specific content of each of the age group, gender, place of residence, interest (major category), interest (medium category), interest (minor category), and trend that shows the highest frequency is 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 who also show a strong interest in the products posted by user A.

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

[0034] Next, we will explain the process of extracting potential purchasers. It is assumed that the process of using an SNS service to purchase a product involves a person becoming very interested in User A or his / her posts, viewing User A's posts multiple times, and then becoming very interested in the products posted by User A, and then moving to an EC site via User A's posts and purchasing the product sold on the EC site.

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

[0036] 10 and 11 show the processing procedure of the human axis method. In step S31, persona data of SNS users created in advance is read from the storage unit 14, i.e., the persona data of a specific user A and each of other users. In step S32, user E, who has the same or similar persona as user A and is considered to be a user with a similar personality profile to user A, is extracted from the other users. User E is the same as user A in all of the following categories: age, gender, place of residence, interests (major categories), interests (medium categories), interests (minor categories), and trends. Alternatively, user E is the same as user A in some categories of age, gender, place of residence, interests (major categories), interests (medium categories), interests (minor categories), and trends, but is the same in other categories. Here, the different categories are pre-set to include categories other than those considered important for showing strong interest in a person, such as place of residence and interests (minor categories). In other words, users whose place of residence and interests (minor categories) are different from those of user A, but whose other items 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 F who has shown a strong interest in user E or the posts is requested from the SNS server 2 via the communication unit 17 and downloaded. In other words, user F who has shown a strong interest in user E, who has a similar personality to specific user A, is assumed to be a user who is likely to also show a strong interest in specific user A. As described above, users F who have shown a strong interest in user E or the posts include followers of user E, users who have expressed sympathy for user E's posts by "liking" or the like, users who have spread user E's posts by retweeting or sharing, users who have saved (downloaded) images posted by user E, users who have posted comments on user E's posts, 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 until now.

[0039] In step S35, data on a typical persona (PA) of user D, who has shown a strong interest in user A or the posts made by user A and who has also shown a strong interest in the products posted by user A, created in advance is read from the storage unit 14, and user H, who has a persona that is the same as 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, gender, 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, such as place of residence and interests (minor categories).

[0040] It is assumed that User H is a user who is highly likely to show a strong interest in a particular User A and to purchase products posted by User A. 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 e-commerce site via User A's posts and ultimately purchase products.

[0041] 12 and 13 show the processing procedure of the first product-based method. First, in step S41, the hashtags attached to images posted by a specific user A are 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 hashtags. 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 similar products d and e.

[0042] In step S44, the account of user J, who posted an image with hashtags 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 hashtags including the names of similar products d and e that are 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 sold at the apparel shop where user A works are relatively consistent with the users' 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 users who have shown a strong interest in the products posted by user A or similar products but have not had any contact with user A until now are assumed to be users.

[0044] In step S47, data of a typical persona (PA) of user D, who has shown a strong interest in user A or the post made by 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 highly likely to become interested in products posted by specific user A in the future, and is expected to be a potential buyer who is likely to become interested in products posted by 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 procedure of the second product axis method. First, in step S51, the hashtags attached to the images posted by a specific user A are 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 hashtags. Of course, the names (product names) of the products posted by user A may also be input by the operator via the input device 18.

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

[0048] In step S54, user N is extracted from user J by excluding user A who downloaded the product in step S21 or user B who shows a strong interest in the product's post. User N is assumed to be a user who has shown 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 has shown a strong interest in the posts of user A, which has been created in advance, is read from the storage unit 14, and user P having a persona that is the same as or similar to the typical persona (PA) is extracted from user N. Here, "similar" is as described above.

[0050] User P is highly likely to become interested in products posted by specific user A in the future, and is expected to be a potential buyer who is interested in products posted by specific user A, moves to an EC site via user A's post, and ultimately purchases 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, who are likely to move to an EC site via user A's posts and ultimately purchase a product, and potential purchasers who are highly likely to show a strong interest in products posted by user A in the future, who are likely to move to an EC site via user A's posts and ultimately purchase a product. Therefore, it is possible to achieve effective advertisement distribution to these potential purchasers.

[0052] Although several 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 within the scope and spirit of the invention, as well as within the scope of the invention described in the claims and their equivalents. [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 persona data of each SNS user who receives the SNS service; a processor that executes the program; an information processing device including a communication unit that communicates with an SNS server that provides the SNS service via an Internet line, The processor executes the program, a means for requesting and downloading, via the communication unit, an account of a second SNS user who posted about a specific product posted by a specific SNS user among the SNS users; a means for requesting and downloading an account of a third SNS user who posted a product similar to the specific product from the SNS server via the communication unit; An information processing device that functions as a means for extracting potential purchasers of the specific product from the second and third SNS users.

2. a storage unit that stores the program and persona data of each SNS user who receives the SNS service; a processor that executes the program; an information processing device including a communication unit that communicates with an SNS server that provides the SNS service 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 from the SNS server via the communication unit; a means for requesting and downloading, via the communication unit, an account of a third SNS user who posted about the specific product posted by the specific SNS user from the SNS server; 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 extracting potential purchasers of the specific product from the fourth SNS users.

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