# Clustering Merchants and Accurate Marketing of Products Using the Segmentation Tree Vector Space Model

Authors: Xuwu Ding; Zhong Wu; Meng Li
Journal: Mathematical Problems in Engineering
 (2022)

---

## Abstract

Using social commerce users as the data source, a reasonable and effective interest expression mechanism is used to construct an interest graph of sample users to achieve the purpose of clustering merchants and users as well as realizing accurate marketing of products. By introducing an improved vector space model, the segmentation tree vector space model, to express the interests of the target user group and, on this basis, using the complex network analysis tool Gephi to construct an interest graph, based on the user interest graph, we use Python to implement the K-means algorithm and the users of the sample set according to interest topics for community discovery. The experimental results show that the interests of the sample users are carefully divided, each user is divided into different thematic communities according to different interests, and the constructed interest graph is more satisfactory. The research design of the social commerce user interest mapping scheme is highly feasible, reasonable, and effective and provides new ideas for the research of interest graph, and the boundaries of thematic communities based on interests are clear.


---

## Body

## 1. Introduction

With the widespread popularity of the Internet, the e-commerce industry has made rapid development. Traditional e-commerce often ignores the user’s interests and needs and simply explores the user’s potential needs from the shopping basket history or browsing traces without paying attention to the influence of social relationships in social media on the user’s shopping behavior. In addition, the continuous innovation of business models has led to the emergence of a large number of goods and various e-commerce platforms; the problems of how to get the goods you need from this large number of goods and how to identify the credibility of e-commerce have become the bottleneck limiting the development of e-commerce model. The emergence of social commerce has achieved a breakthrough of this bottleneck, and its media, social, and business attributes can well realize the combination of social media and e-commerce and carry out business activities based on the interests of users. Therefore, the key step to the success of social commerce is to explore the interest graph of users in e-commerce and realize the gathering of interest-themed communities.Using social circles, an interest graph can be utilized to attract a wide range of people. On the one hand, it may achieve accurate product marketing by grouping and clustering users’ interests; on the other hand, it can build a community with the same or comparable interests as the core, allowing users and merchants to communicate more effectively. This study starts with user interest modeling, then builds the text expressing user interests using the upgraded VSM, segmentation tree VSM, and then uses the complex network analysis tool Gephi to generate the interest graph. Data mining algorithms are utilized to realize the discovery of topic communities based on this.


### 1.1. Personalized Recommendation

Traditional e-commerce sites’ personalized recommendations suffer from data scarcity and a lack of complete knowledge about users’ interests; however, cross-domain recommendations based on e-commerce sites and community mining can be a useful alternative. Study [1] outlined the drawbacks of similarity recommendation based on customer ties in existing social networks and offered a cross-domain recommendation system that included social networking sites like Facebook. Study [2] considered the multidimensional nature of customer information to recommend other domains based on customer preferences for a specific domain. Study [3] improved the accuracy of product recommendation by defining the cross-domain personality trait classification problem and using the predictive text embedding method to introduce the user’s personality traits into cross-domain recommendation. Study [4] integrated interests from different domains using the Latent Dirichlet Allocation ensemble probability model (LDA) to achieve cross-domain personalized recommendation. Study [5] proposed a cross-domain recommendation system framework based on Folksonomies. The premise of the cross-domain recommendation algorithm is similar to that of the collaborative filtering algorithm, which primarily addresses two issues. The first is the detection and integration of cross-domain users’ knowledge. Study [6] constructed a set of cross-system unified user ontology modeling and identification theory. Study [7] focused on the user modeling problem and proposed a context-aware user modeling theory based on context-awareness as well as the FOAF (Friend of a Friend) standard and cross-social network user model Mypes. The other is the cross-domain data integration problem. Study [8] analyzed the representative Linked Data databases Open Linked Data including DBpedia, Freebase, and Linked-MDB, which have automatically or semiautomatically converted the data of traditional web pages into Linked Data, among which DBpedia is one of the world’s largest multidomain knowledge ontologies and is widely used for cross-domain recommendations. However, these data are mainly focused on information retrieval and news domains and less involved in the domain of product recommendation [9].Although the field of personalized recommendation is mature in terms of algorithm and technology, the training data set is from a single source, primarily focusing on users’ browsing records and shopping basket records, and lacks the support of users’ interests, according to the summary analysis of the above literature. From the perspective of developing a user interest graph, this work addresses this weakness and adds to the field of product suggestion in personalized recommendation.


### 1.2. Interest Graph

As the core element of cross-domain recommendation, the rationality of user interest graph construction will directly affect the effectiveness of the recommendation algorithm. There are various ways to classify user interests, which are generally divided into “long-term interests” and “short-term interests.” Study [10] classified interests into one-time interests, long-term constant interests, periodic interests, periodic instantaneous interests, and irregular interests. The basic principle of interest recommendation is based on content, collaborative filtering, and similar labeling methods, focusing on the problem of interest graph construction. Study [11] pointed out that “social graph” is based on your personal acquaintance social connection, so the circle is limited; while “interest graph” is based on common interests, there is no need to know each other, so it greatly extends the depth and breadth of social interaction, which has the following features: (a) one-way focus, not two-way friends; (b) organization around shared interests, not personal real social relationships; (c) default public, not default private; (d) common struggle: it does not matter what you were or what you are; what matters is what you will be. The process of building user interest graph can be divided into two steps: firstly, interest mining process, the traditional user interest mining is based on the user’s historical information (such as commodity browsing path) way [12], along with the emergence of social networks Facebook, Microblog, and so on, broadened the channel of interest mining, there is user interest modeling and interest mining [13]; the other is the interest graph integration process. Accenture believes that recommendations can be made by acquiring customers’ interests in different websites, such as recommending luxury off-road vehicles with LCD screens to customers when they know that they love excursions and skiing, prefer Rolex watches, and have purchased a tablet computer. The Digital Enterprise Research Institute (DERI) at the National University of Galway in Ireland proposed the concept of semantic-based customer interest mapping modeling across websites [14], where they obtained complete customer interest graphs by integrating customer information shared on private websites and made recommendations using hybrid link prediction and content-based diffusion activation methods [15], and the study [16] proposed the concept of group interest. Studies [17, 18] employed a user interest graph for user community segmentation and proposed a method for assessing user influence on social media based on distinct interest areas.Study [19] proposed a basic approach to construct interest mapping by analyzing a large number of social media, in which interest selection, classification, data collection, and interest integration are the main steps. Study [20], on the other hand, applied the ensemble probability model to the integration of social network users’ interests and realized a personalized information push service. In terms of stimulating user emotions, interest mapping can effectively stimulate the positive emotions of users [21]. Studies [22, 23]found that personalized news push has data sparsely problems, and to solve such problems, it has been used to improve the satisfaction of news push audience by constructing short- and long-term interest models of users to clarify the interaction between users, news, and potential topics. Study [24] proposed a dynamic Top-K interest subgraph discovery method with large-scale labeled graphs (commonly used in information networks, biological networks, etc.), which can effectively find users’ interest graphs and cluster them in a large-scale network space.The researchers mentioned above studied interest graph from many angles and developed ways for personalizing recommendations, stimulating users’ emotions, and increasing traffic using interest graph. They did not, however, focus on how to design an interest graph, and some of them just proposed certain imagined stages without putting them into action. As a result, in this research, we offer a user interest modeling mechanism in personalized services and utilize Gephi, a complex network analysis software, to build interest mapping of social commerce users and study the thematic communities of social commerce from this perspective.


## 1.1. Personalized Recommendation

Traditional e-commerce sites’ personalized recommendations suffer from data scarcity and a lack of complete knowledge about users’ interests; however, cross-domain recommendations based on e-commerce sites and community mining can be a useful alternative. Study [1] outlined the drawbacks of similarity recommendation based on customer ties in existing social networks and offered a cross-domain recommendation system that included social networking sites like Facebook. Study [2] considered the multidimensional nature of customer information to recommend other domains based on customer preferences for a specific domain. Study [3] improved the accuracy of product recommendation by defining the cross-domain personality trait classification problem and using the predictive text embedding method to introduce the user’s personality traits into cross-domain recommendation. Study [4] integrated interests from different domains using the Latent Dirichlet Allocation ensemble probability model (LDA) to achieve cross-domain personalized recommendation. Study [5] proposed a cross-domain recommendation system framework based on Folksonomies. The premise of the cross-domain recommendation algorithm is similar to that of the collaborative filtering algorithm, which primarily addresses two issues. The first is the detection and integration of cross-domain users’ knowledge. Study [6] constructed a set of cross-system unified user ontology modeling and identification theory. Study [7] focused on the user modeling problem and proposed a context-aware user modeling theory based on context-awareness as well as the FOAF (Friend of a Friend) standard and cross-social network user model Mypes. The other is the cross-domain data integration problem. Study [8] analyzed the representative Linked Data databases Open Linked Data including DBpedia, Freebase, and Linked-MDB, which have automatically or semiautomatically converted the data of traditional web pages into Linked Data, among which DBpedia is one of the world’s largest multidomain knowledge ontologies and is widely used for cross-domain recommendations. However, these data are mainly focused on information retrieval and news domains and less involved in the domain of product recommendation [9].Although the field of personalized recommendation is mature in terms of algorithm and technology, the training data set is from a single source, primarily focusing on users’ browsing records and shopping basket records, and lacks the support of users’ interests, according to the summary analysis of the above literature. From the perspective of developing a user interest graph, this work addresses this weakness and adds to the field of product suggestion in personalized recommendation.


## 1.2. Interest Graph

As the core element of cross-domain recommendation, the rationality of user interest graph construction will directly affect the effectiveness of the recommendation algorithm. There are various ways to classify user interests, which are generally divided into “long-term interests” and “short-term interests.” Study [10] classified interests into one-time interests, long-term constant interests, periodic interests, periodic instantaneous interests, and irregular interests. The basic principle of interest recommendation is based on content, collaborative filtering, and similar labeling methods, focusing on the problem of interest graph construction. Study [11] pointed out that “social graph” is based on your personal acquaintance social connection, so the circle is limited; while “interest graph” is based on common interests, there is no need to know each other, so it greatly extends the depth and breadth of social interaction, which has the following features: (a) one-way focus, not two-way friends; (b) organization around shared interests, not personal real social relationships; (c) default public, not default private; (d) common struggle: it does not matter what you were or what you are; what matters is what you will be. The process of building user interest graph can be divided into two steps: firstly, interest mining process, the traditional user interest mining is based on the user’s historical information (such as commodity browsing path) way [12], along with the emergence of social networks Facebook, Microblog, and so on, broadened the channel of interest mining, there is user interest modeling and interest mining [13]; the other is the interest graph integration process. Accenture believes that recommendations can be made by acquiring customers’ interests in different websites, such as recommending luxury off-road vehicles with LCD screens to customers when they know that they love excursions and skiing, prefer Rolex watches, and have purchased a tablet computer. The Digital Enterprise Research Institute (DERI) at the National University of Galway in Ireland proposed the concept of semantic-based customer interest mapping modeling across websites [14], where they obtained complete customer interest graphs by integrating customer information shared on private websites and made recommendations using hybrid link prediction and content-based diffusion activation methods [15], and the study [16] proposed the concept of group interest. Studies [17, 18] employed a user interest graph for user community segmentation and proposed a method for assessing user influence on social media based on distinct interest areas.Study [19] proposed a basic approach to construct interest mapping by analyzing a large number of social media, in which interest selection, classification, data collection, and interest integration are the main steps. Study [20], on the other hand, applied the ensemble probability model to the integration of social network users’ interests and realized a personalized information push service. In terms of stimulating user emotions, interest mapping can effectively stimulate the positive emotions of users [21]. Studies [22, 23]found that personalized news push has data sparsely problems, and to solve such problems, it has been used to improve the satisfaction of news push audience by constructing short- and long-term interest models of users to clarify the interaction between users, news, and potential topics. Study [24] proposed a dynamic Top-K interest subgraph discovery method with large-scale labeled graphs (commonly used in information networks, biological networks, etc.), which can effectively find users’ interest graphs and cluster them in a large-scale network space.The researchers mentioned above studied interest graph from many angles and developed ways for personalizing recommendations, stimulating users’ emotions, and increasing traffic using interest graph. They did not, however, focus on how to design an interest graph, and some of them just proposed certain imagined stages without putting them into action. As a result, in this research, we offer a user interest modeling mechanism in personalized services and utilize Gephi, a complex network analysis software, to build interest mapping of social commerce users and study the thematic communities of social commerce from this perspective.


## 2. Materials and Methods

The study takes the Chinese social commerce platform “Mogu” (Mogu is positioned as a new type of buyer community, focusing on providing a Chinese social commerce platform for discovering beauty and fashion, sharing shopping fun, making friends with like-minded people, and communicating freely. Users browse Mogu to find their favorite items and then link to Taobao (China’s largest C2C e-commerce platform) to share both the fun of shopping and their various creations on the online store) as an example, uses the web crawler technology based on HttpClient and HtmlParser [25, 26] components to obtain the research data, then uses the user interest modeling method in personalization service to express the user’s interest in the form of text, and introduces the social network relationship values into the calculation of user interests to realize the extraction and integration of user interests, so as to get the comprehensive interests of users. Based on this, we use Gephi, a visual complex network analysis tool, to construct a user’s interest graph. Figure 1 shows the program flowchart.Figure 1
Program flowchart.


### 2.1. USER Interest Extraction

The social commerce site selected for the study is Mogu, an e-commerce platform similar to Pinterest [27] social photo sharing. The users of this platform are involved in shopping, food, photography, sports, and many other aspects, and they share their favorite products or experiences on social media while shopping, thus increasing the popularity of the social commerce platform. Therefore, this study selects users of Mogu as the source of data based on the following principles: (1) the user base is relatively large; (2) the platform is developing well; (3) the data collection is convenient, and the data format is relatively uniform.By analyzing the structure of the Mogu website, user interests can be extracted from the following four areas.


#### 2.1.1. Personal Tags

Personal tags are simple descriptions of users’ basic attributes, such as their profession, interests, and areas of expertise. In social commerce, personal tags can be used to quickly match users with each other and find “like-minded” friends or “opinion leaders” who can provide reference advice.


#### 2.1.2. Following

According to the classic Pareto principle, 80% of the content on the web is created by 20% of the people, and the same is true in social commerce. The purpose of users using social commerce is to get information and suggestions, so users’ following behavior becomes the most important way to get their interest.


#### 2.1.3. Sharing

The sharing behavior of social commerce users focuses on the sharing of products, shopping experience, and store evaluation, and the content of sharing is mainly based on images and supplemented by text. As the topics shared by users may change frequently, it is easy to cause drift when extracting.


#### 2.1.4. Liking

Like content contains a more precise amount of information compared to shared content, not only limited to products, shopping experience, store reviews, and so on but also including interesting pictures, artwork, and celebrities.


### 2.2. User Interest Representation

In order to better describe users’ interests, it is necessary to analyze them quantitatively and assign different weights to different interests to measure them more accurately. Therefore, in order to build the interest graph of social commerce users, this paper adopts the segmentation tree vector space model (a modified vector space model (VSM)) [28–33]. This model can classify and assign weights to different interests of users, that is, expressing users’ interests in the form of text, then extracting interest keywords from them for classification, and then calculating the interest distribution by weights. This representation can both visually represent user interests and initially cluster users according to thematic interest categories to facilitate subsequent community discovery [34–37]. Figure 2 presents the segmentation tree vector space model.Figure 2
Segmentation tree vector space model.Thus, the user interest in social e-commerce can be expressed as(1)UIM=SI1,W1SI2,W2,…,SIn,Wn,where SIk denotes the user’s topic interest class and Wk denotes the weight of SIk. The specific calculation method will be described in detail later.


### 2.3. Calculation of User Interest

In order to facilitate the organization and generalization of the collected data and the subsequent research, the following assumptions were made in calculating user interest for this study:(a)
There is no distinction between long-term and short-term user interests(b)
Users’ interests do not drift(c)
Users’ interests are represented by the content they share and likeThe basic idea of the algorithm: the user’s interest is divided into individual interest and group interest to be calculated separately and then synthesized by a parameter with a value between [0∼1]. Among them, the calculation of individual interest is mainly derived from personal tags, followers, shares, and favorite content. By sorting the collected data, the key texts expressing users’ interests are extracted, then the TF-IDF algorithm is used to calculate the values of each part, and then the weighted summation is performed. The group interest is calculated by introducing the social relationship value and the importance of the user based on the completed calculation of the individual interest, and the three parts are weighted and summed.As shown in Figure2, user interest UkIM can be represented by the following equation:(2)UkIM=αIndividualUkIM+1−αGroupsUkIM,where Individual UkIM denotes individual interest and Groups UkIM denotes the group interest of the user. α is the adjustment coefficient, owing to the fact that the user’s interest is divided into two parts: individual interest and group interest, with individual interest occupying the dominating position in previous studies, and when paired with the actual data obtained, the final determination is 0.75. And the individual interest of the user can be expressed as(3)IndividualUkIM=SIi,hWtagik,Wfollowik,Wshareik,Wlikeik,where SIi denotes the i-th interest component of the user and the function hWtagik,Wfollowik,Wshareik,Wlikeik represents the weight of SIi, which is calculated by the formula(4)hWtagik,Wfollowik,Wshareik,Wlikeik=βWtagik+γWfollowik+δWshareik+εWlikeik,where(5)Wtagik=∑ntagiNtag,wherentagi denotes the number of tags in the user tags that indicate topic interest SIi and Ntag represents the total number of tags for the user.(6)Wfollowik=∑nfollowiNfollow,where nfollowi is the number of users’ followers who also have topic interest SIi and Nfollow denotes the number of users’ followings.(7)Wshareik=∑nshareiNshare,where nsharei denotes the number of SIi about the topic interest among all the content shared by the user and Nshare is the total amount shared by the user.(8)Wlikeik=∑nlikeiNlike,where nlikei represents the number of SIi about the topic interest in the content that the user likes and Nlike represents its total amount.Also in (4), β+γ+δ+ε=1 indicates that, for different users, the weight assignments of the above four terms will vary. The four features of each user’s tag set, the category of the item of interest, the material shared, and the content liked, must be restricted by the values of β, γ, δ, and ε. Because the tag set is so significant in determining users’ interests, β is given a value of 0.4, and γ, δ, and ε are given an average value of 0.2 and a threshold value of 0.1 for the range of the above three values. If user A’s tag set is (astrologer, dresser, photographer) and the objects of interest and favorite content are mostly in these three sets, but the material shared is less (the amount of content shared is 20), then the values of γ and ε are 0.3, and the value of δ is 0.In (2), Groups UkIM represents the interest of the user’s group, and in calculating it, we need to consider the value of social relations between users SR and the importance of users UW.(9)SRU1,U2=OU1∩​OU2OU1∪​OU2,where OU1 denotes the objects associated with U1 (where the objects not only are users but also include interests) and OU2 denotes the objects associated with U2. OU1∩​OU2 denotes the number of objects jointly associated with two users, and OU1∪​OU2 denotes the total number of objects jointly associated with two users.(10)UWk=1−d+dUWp1Followp1+UWp2Followp2+⋯+UWpmFollowp1m,where p1, p2, …, pm are the followers of user k, Follow (pm) is the number of followers of user pm, and the damping factor, d, has a value between 0 and 1, and it indicates the likelihood that user k will continue to click into another user’s space. The damping factor is used since it is impossible for a user to read all of the content provided by all of his followers. In social media, the damping factor is mainly applied in the process of calculating the PageRank value, which generally takes the value of 0.75.So the group interest can be expressed as(11)GroupsUkIM=∑j∈HSRUk,Uj∗UWj∗IndividualUjIM.IndividualUjIM denotes the individual interest of user Uj, H denotes the interest group in which Uk is located, and SRU1,U2 denotes the social relationship values of users Uj and Uk. UWj denotes the importance of user Uj.


## 2.1. USER Interest Extraction

The social commerce site selected for the study is Mogu, an e-commerce platform similar to Pinterest [27] social photo sharing. The users of this platform are involved in shopping, food, photography, sports, and many other aspects, and they share their favorite products or experiences on social media while shopping, thus increasing the popularity of the social commerce platform. Therefore, this study selects users of Mogu as the source of data based on the following principles: (1) the user base is relatively large; (2) the platform is developing well; (3) the data collection is convenient, and the data format is relatively uniform.By analyzing the structure of the Mogu website, user interests can be extracted from the following four areas.


### 2.1.1. Personal Tags

Personal tags are simple descriptions of users’ basic attributes, such as their profession, interests, and areas of expertise. In social commerce, personal tags can be used to quickly match users with each other and find “like-minded” friends or “opinion leaders” who can provide reference advice.


### 2.1.2. Following

According to the classic Pareto principle, 80% of the content on the web is created by 20% of the people, and the same is true in social commerce. The purpose of users using social commerce is to get information and suggestions, so users’ following behavior becomes the most important way to get their interest.


### 2.1.3. Sharing

The sharing behavior of social commerce users focuses on the sharing of products, shopping experience, and store evaluation, and the content of sharing is mainly based on images and supplemented by text. As the topics shared by users may change frequently, it is easy to cause drift when extracting.


### 2.1.4. Liking

Like content contains a more precise amount of information compared to shared content, not only limited to products, shopping experience, store reviews, and so on but also including interesting pictures, artwork, and celebrities.


## 2.1.1. Personal Tags

Personal tags are simple descriptions of users’ basic attributes, such as their profession, interests, and areas of expertise. In social commerce, personal tags can be used to quickly match users with each other and find “like-minded” friends or “opinion leaders” who can provide reference advice.


## 2.1.2. Following

According to the classic Pareto principle, 80% of the content on the web is created by 20% of the people, and the same is true in social commerce. The purpose of users using social commerce is to get information and suggestions, so users’ following behavior becomes the most important way to get their interest.


## 2.1.3. Sharing

The sharing behavior of social commerce users focuses on the sharing of products, shopping experience, and store evaluation, and the content of sharing is mainly based on images and supplemented by text. As the topics shared by users may change frequently, it is easy to cause drift when extracting.


## 2.1.4. Liking

Like content contains a more precise amount of information compared to shared content, not only limited to products, shopping experience, store reviews, and so on but also including interesting pictures, artwork, and celebrities.


## 2.2. User Interest Representation

In order to better describe users’ interests, it is necessary to analyze them quantitatively and assign different weights to different interests to measure them more accurately. Therefore, in order to build the interest graph of social commerce users, this paper adopts the segmentation tree vector space model (a modified vector space model (VSM)) [28–33]. This model can classify and assign weights to different interests of users, that is, expressing users’ interests in the form of text, then extracting interest keywords from them for classification, and then calculating the interest distribution by weights. This representation can both visually represent user interests and initially cluster users according to thematic interest categories to facilitate subsequent community discovery [34–37]. Figure 2 presents the segmentation tree vector space model.Figure 2
Segmentation tree vector space model.Thus, the user interest in social e-commerce can be expressed as(1)UIM=SI1,W1SI2,W2,…,SIn,Wn,where SIk denotes the user’s topic interest class and Wk denotes the weight of SIk. The specific calculation method will be described in detail later.


## 2.3. Calculation of User Interest

In order to facilitate the organization and generalization of the collected data and the subsequent research, the following assumptions were made in calculating user interest for this study:(a)
There is no distinction between long-term and short-term user interests(b)
Users’ interests do not drift(c)
Users’ interests are represented by the content they share and likeThe basic idea of the algorithm: the user’s interest is divided into individual interest and group interest to be calculated separately and then synthesized by a parameter with a value between [0∼1]. Among them, the calculation of individual interest is mainly derived from personal tags, followers, shares, and favorite content. By sorting the collected data, the key texts expressing users’ interests are extracted, then the TF-IDF algorithm is used to calculate the values of each part, and then the weighted summation is performed. The group interest is calculated by introducing the social relationship value and the importance of the user based on the completed calculation of the individual interest, and the three parts are weighted and summed.As shown in Figure2, user interest UkIM can be represented by the following equation:(2)UkIM=αIndividualUkIM+1−αGroupsUkIM,where Individual UkIM denotes individual interest and Groups UkIM denotes the group interest of the user. α is the adjustment coefficient, owing to the fact that the user’s interest is divided into two parts: individual interest and group interest, with individual interest occupying the dominating position in previous studies, and when paired with the actual data obtained, the final determination is 0.75. And the individual interest of the user can be expressed as(3)IndividualUkIM=SIi,hWtagik,Wfollowik,Wshareik,Wlikeik,where SIi denotes the i-th interest component of the user and the function hWtagik,Wfollowik,Wshareik,Wlikeik represents the weight of SIi, which is calculated by the formula(4)hWtagik,Wfollowik,Wshareik,Wlikeik=βWtagik+γWfollowik+δWshareik+εWlikeik,where(5)Wtagik=∑ntagiNtag,wherentagi denotes the number of tags in the user tags that indicate topic interest SIi and Ntag represents the total number of tags for the user.(6)Wfollowik=∑nfollowiNfollow,where nfollowi is the number of users’ followers who also have topic interest SIi and Nfollow denotes the number of users’ followings.(7)Wshareik=∑nshareiNshare,where nsharei denotes the number of SIi about the topic interest among all the content shared by the user and Nshare is the total amount shared by the user.(8)Wlikeik=∑nlikeiNlike,where nlikei represents the number of SIi about the topic interest in the content that the user likes and Nlike represents its total amount.Also in (4), β+γ+δ+ε=1 indicates that, for different users, the weight assignments of the above four terms will vary. The four features of each user’s tag set, the category of the item of interest, the material shared, and the content liked, must be restricted by the values of β, γ, δ, and ε. Because the tag set is so significant in determining users’ interests, β is given a value of 0.4, and γ, δ, and ε are given an average value of 0.2 and a threshold value of 0.1 for the range of the above three values. If user A’s tag set is (astrologer, dresser, photographer) and the objects of interest and favorite content are mostly in these three sets, but the material shared is less (the amount of content shared is 20), then the values of γ and ε are 0.3, and the value of δ is 0.In (2), Groups UkIM represents the interest of the user’s group, and in calculating it, we need to consider the value of social relations between users SR and the importance of users UW.(9)SRU1,U2=OU1∩​OU2OU1∪​OU2,where OU1 denotes the objects associated with U1 (where the objects not only are users but also include interests) and OU2 denotes the objects associated with U2. OU1∩​OU2 denotes the number of objects jointly associated with two users, and OU1∪​OU2 denotes the total number of objects jointly associated with two users.(10)UWk=1−d+dUWp1Followp1+UWp2Followp2+⋯+UWpmFollowp1m,where p1, p2, …, pm are the followers of user k, Follow (pm) is the number of followers of user pm, and the damping factor, d, has a value between 0 and 1, and it indicates the likelihood that user k will continue to click into another user’s space. The damping factor is used since it is impossible for a user to read all of the content provided by all of his followers. In social media, the damping factor is mainly applied in the process of calculating the PageRank value, which generally takes the value of 0.75.So the group interest can be expressed as(11)GroupsUkIM=∑j∈HSRUk,Uj∗UWj∗IndividualUjIM.IndividualUjIM denotes the individual interest of user Uj, H denotes the interest group in which Uk is located, and SRU1,U2 denotes the social relationship values of users Uj and Uk. UWj denotes the importance of user Uj.


## 3. Results and Discussion

### 3.1. User Interest Model Construction and Interest Value Calculation

The program collected the public information of Mogu users and obtained the information of 328 influencers in total, each of which has at least 5,000 fans. Considering the carrying capacity of the database and the efficiency of the analysis software, 20 of these fans were randomly selected for data collection. A total of 6840 pieces of data information were collected, and 5454 users’ information was collected by eliminating the duplicated and useless data. By processing these 5454 users’ sharing contents, following magazine contents, personal tags, and favorite contents, and using ICTCLAS, a word division system of CAS, for word division and word annotation, we found that the interests of the sample users were concentrated in the following fields: apparel, street shot, constellation, fitness, tourism, cosmetology, and photography. The specific breakdown of each interest is shown in Figure3.Figure 3
User interest segmentation tree.Take user 136592 as an example; after using the NLTK library in Python to split words, we found that he has three personal tags, namely “astrologer,” “clothing matching,” and “photography enthusiast.” After analyzing his 1355 texts, we found 846 texts reflecting his interests, which were scattered in 5 interest sets, including 387 in constellation, 220 in apparel, 150 in photography, 50 in cosmetology, and 39 in ornament. At the same time, the analysis of their followings and followings of the magazine was carried out, the top 15 users were extracted according to the weighted values of social relationship value and user importance to form the 136592 interest groups, the maximum weighted value of these 15 users was 0.82, and the minimum was 0.39.After obtaining the above information, the algorithm involved in the study was used to find the interest model of user 136592 as {(astrology, 0.41), (vintage, 0.18), (fashion, 0.02), (scenery, 0.23), (skincare, 0.06), (group purchase, 0.08), (glasses, 0.08)}.


### 3.2. Constructing Interest Graph

Before constructing an interest graph using Gephi, the interests of the entire sample set of users need to be processed briefly because Gephi only accepts two types of csv files, namely, edge data csv files and point data csv files. In order to better construct the interest graph of the sample users, edge data files are used in this study; that is, edges are constructed between each user and the interests they have. Through filtering, a total of 3193 nodes as well as 9739 edges were obtained (in the interest graph, the nodes are divided into two categories, one is the user’s ID number and the other is the interest topic, and the connecting line between two nodes indicates which interests the user has). The interest graph is as follows.In Figure4, we can see that the users in the sample are divided into different communities according to their interests. The size of the font in the figure is divided by the “modularity” index in Gephi; from the figure, we can see that fashion and folk in street photography have very high modularity value; through the analysis of the Mushroom Street website, we also found that a large part of its content is about some trend, fashion picture sharing. There are some intermediate users who play the role of bridge between different interest communities.Figure 4
Mogu users interest graph.


### 3.3. Community Discovery

Figure5 shows that, using the modularity function in Gephi, it can be derived that the user interest profile can be roughly divided into 8 aggregation zones. Therefore, for the sample users, the initial number of cores of mass in the K-means algorithm can be set to 8. To verify its reasonableness, the study introduces the silhouette coefficient, which takes values in the range of [−1, 1], and the larger the value, the better the clustering effect. The implementation code of the silhouette coefficient is written using Python, and a graph of the relationship between the silhouette coefficient and the K-means core number (K-value for short) can be derived, as shown in Figure 6. From the graph, it can be seen that the silhouette coefficient has a maximum value when K-value = 8. This further validates the feasibility and effectiveness of using an interest graph to determine the K-value.Figure 5
Modularization results of the interest graph.Figure 6
Plot of silhouette coefficient versus K-value.After the K-value was determined, the K-means algorithm was implemented in Python to perform community discovery on the sample, and the results were as follows.As seen in Table1, the square sum of the distance between clusters accounts for 81.25% of the square sum of overall distance, and these data also indicate that the clustering between different clusters achieves the maximum. Therefore, the clustering effect is good.Table 1
The square sum of internal distance in every cluster.
Cluster12345678Square sum of internal distance1.63572.52345.32574.47121.24780.89742.25872.4244between_SS/total_SS81.25%Table2 shows the comparison of the mean values of each indicator in different clusters, and it can be seen from the table that the differences between different clusters are very obvious, which further verify the validity of the cluster analysis results.Table 2
The table of variance analysis.
ClusterErrorFSig.Mean squaredfMean squaredfMix8.83170.00132507741.034≤0.001Vintage0.29070.005325052.814≤0.001Lovely5.99970.00232502665.849≤0.001Sports and leisure1.72570.0143250126.376≤0.001European style2.73470.0163250174.567≤0.001Fresh1.84370.0143250127.430≤0.001Ripe young3.68970.0163250235.727≤0.001Fashion18.44870.00432504478.154≤0.001Tradition18.91470.00432505180.555≤0.001Divination0.38270.011325034.891≤0.001Astrology0.39570.011325037.399≤0.001Fortune0.34870.009325037.199≤0.001Experience0.46970.014325034.147≤0.001Course0.46570.014325033.870≤0.001Achievement0.46770.014325034.030≤0.001Skincare0.17270.008325022.637≤0.001Makeup0.10670.005325021.121≤0.001Recommendation0.09870.004325021.824≤0.001Travel guide0.23470.005325044.024≤0.001Group purchase0.38870.010325040.709≤0.001Equipment0.90270.020325045.759≤0.001Character7.76670.00132507754.478≤0.001Scenery7.74470.00132507117.170≤0.001Jewelry0.49270.011325045.012≤0.001Glasses0.48470.011325044.444≤0.001Others0.48170.011325044.134≤0.001Finally, the results of community discovery using K-means are shown in Figure7, from which it can be seen that the sample set is divided into 8 communities of different sizes.Figure 7
Community discovery by using K-means.


### 3.4. Analysis of Results

The results from the community discovery show that there is a high degree of coupling between the communities internally, indicating that the members within each community in Mogu have a high degree of association with each other, and the same conclusion can be drawn from the interest graph. In these interest communities, Mogu’s influencers act as community opinion leaders, which is also an important development direction of social commerce at present. Through the mining, operation, and maintenance of the influencers, these influencers are encouraged to share their shopping experience more often to attract customers. In addition, the analysis of community members reveals that a large part of them come from Weibo, Qzone, and so on, which also reflects its social characteristics. The obvious differences between communities allow users to quickly and accurately find the right community for them based on their interests and to get the information they need from the community members. This is the biggest benefit that a social commerce platform like Mogu brings to its users. Moreover, as we can see from the previous study, Mogu has carefully categorized users’ interests to satisfy different interest groups as much as possible.Although the connection between the members of the community in Mogu is relatively close, the connection between the community is relatively small, which also reflects the lack of development of most of the social commerce platforms like Mogu in China. As a representative of domestic social commerce platforms, the development of Mogu has its own characteristics. First, the entrance of Mogu users is mainly Taobao, Baidu, Weibo, and social networking sites. Second, the core of social commerce development is a common interest, and the source of development is driven by some commonality in users themselves and their temperament. Although the characteristics of each community in Mogu are more prominent, the connection between communities is not close enough, which leads to the inability to effectively use user resources within the whole platform. Third, the source of commodity information is relatively single, most of the commodity purchase links in Mogu are from Taobao, and there is no good connection with other shopping sites, which also leads to the homogenization of information on the platform which is more serious.


## 3.1. User Interest Model Construction and Interest Value Calculation

The program collected the public information of Mogu users and obtained the information of 328 influencers in total, each of which has at least 5,000 fans. Considering the carrying capacity of the database and the efficiency of the analysis software, 20 of these fans were randomly selected for data collection. A total of 6840 pieces of data information were collected, and 5454 users’ information was collected by eliminating the duplicated and useless data. By processing these 5454 users’ sharing contents, following magazine contents, personal tags, and favorite contents, and using ICTCLAS, a word division system of CAS, for word division and word annotation, we found that the interests of the sample users were concentrated in the following fields: apparel, street shot, constellation, fitness, tourism, cosmetology, and photography. The specific breakdown of each interest is shown in Figure3.Figure 3
User interest segmentation tree.Take user 136592 as an example; after using the NLTK library in Python to split words, we found that he has three personal tags, namely “astrologer,” “clothing matching,” and “photography enthusiast.” After analyzing his 1355 texts, we found 846 texts reflecting his interests, which were scattered in 5 interest sets, including 387 in constellation, 220 in apparel, 150 in photography, 50 in cosmetology, and 39 in ornament. At the same time, the analysis of their followings and followings of the magazine was carried out, the top 15 users were extracted according to the weighted values of social relationship value and user importance to form the 136592 interest groups, the maximum weighted value of these 15 users was 0.82, and the minimum was 0.39.After obtaining the above information, the algorithm involved in the study was used to find the interest model of user 136592 as {(astrology, 0.41), (vintage, 0.18), (fashion, 0.02), (scenery, 0.23), (skincare, 0.06), (group purchase, 0.08), (glasses, 0.08)}.


## 3.2. Constructing Interest Graph

Before constructing an interest graph using Gephi, the interests of the entire sample set of users need to be processed briefly because Gephi only accepts two types of csv files, namely, edge data csv files and point data csv files. In order to better construct the interest graph of the sample users, edge data files are used in this study; that is, edges are constructed between each user and the interests they have. Through filtering, a total of 3193 nodes as well as 9739 edges were obtained (in the interest graph, the nodes are divided into two categories, one is the user’s ID number and the other is the interest topic, and the connecting line between two nodes indicates which interests the user has). The interest graph is as follows.In Figure4, we can see that the users in the sample are divided into different communities according to their interests. The size of the font in the figure is divided by the “modularity” index in Gephi; from the figure, we can see that fashion and folk in street photography have very high modularity value; through the analysis of the Mushroom Street website, we also found that a large part of its content is about some trend, fashion picture sharing. There are some intermediate users who play the role of bridge between different interest communities.Figure 4
Mogu users interest graph.


## 3.3. Community Discovery

Figure5 shows that, using the modularity function in Gephi, it can be derived that the user interest profile can be roughly divided into 8 aggregation zones. Therefore, for the sample users, the initial number of cores of mass in the K-means algorithm can be set to 8. To verify its reasonableness, the study introduces the silhouette coefficient, which takes values in the range of [−1, 1], and the larger the value, the better the clustering effect. The implementation code of the silhouette coefficient is written using Python, and a graph of the relationship between the silhouette coefficient and the K-means core number (K-value for short) can be derived, as shown in Figure 6. From the graph, it can be seen that the silhouette coefficient has a maximum value when K-value = 8. This further validates the feasibility and effectiveness of using an interest graph to determine the K-value.Figure 5
Modularization results of the interest graph.Figure 6
Plot of silhouette coefficient versus K-value.After the K-value was determined, the K-means algorithm was implemented in Python to perform community discovery on the sample, and the results were as follows.As seen in Table1, the square sum of the distance between clusters accounts for 81.25% of the square sum of overall distance, and these data also indicate that the clustering between different clusters achieves the maximum. Therefore, the clustering effect is good.Table 1
The square sum of internal distance in every cluster.
Cluster12345678Square sum of internal distance1.63572.52345.32574.47121.24780.89742.25872.4244between_SS/total_SS81.25%Table2 shows the comparison of the mean values of each indicator in different clusters, and it can be seen from the table that the differences between different clusters are very obvious, which further verify the validity of the cluster analysis results.Table 2
The table of variance analysis.
ClusterErrorFSig.Mean squaredfMean squaredfMix8.83170.00132507741.034≤0.001Vintage0.29070.005325052.814≤0.001Lovely5.99970.00232502665.849≤0.001Sports and leisure1.72570.0143250126.376≤0.001European style2.73470.0163250174.567≤0.001Fresh1.84370.0143250127.430≤0.001Ripe young3.68970.0163250235.727≤0.001Fashion18.44870.00432504478.154≤0.001Tradition18.91470.00432505180.555≤0.001Divination0.38270.011325034.891≤0.001Astrology0.39570.011325037.399≤0.001Fortune0.34870.009325037.199≤0.001Experience0.46970.014325034.147≤0.001Course0.46570.014325033.870≤0.001Achievement0.46770.014325034.030≤0.001Skincare0.17270.008325022.637≤0.001Makeup0.10670.005325021.121≤0.001Recommendation0.09870.004325021.824≤0.001Travel guide0.23470.005325044.024≤0.001Group purchase0.38870.010325040.709≤0.001Equipment0.90270.020325045.759≤0.001Character7.76670.00132507754.478≤0.001Scenery7.74470.00132507117.170≤0.001Jewelry0.49270.011325045.012≤0.001Glasses0.48470.011325044.444≤0.001Others0.48170.011325044.134≤0.001Finally, the results of community discovery using K-means are shown in Figure7, from which it can be seen that the sample set is divided into 8 communities of different sizes.Figure 7
Community discovery by using K-means.


## 3.4. Analysis of Results

The results from the community discovery show that there is a high degree of coupling between the communities internally, indicating that the members within each community in Mogu have a high degree of association with each other, and the same conclusion can be drawn from the interest graph. In these interest communities, Mogu’s influencers act as community opinion leaders, which is also an important development direction of social commerce at present. Through the mining, operation, and maintenance of the influencers, these influencers are encouraged to share their shopping experience more often to attract customers. In addition, the analysis of community members reveals that a large part of them come from Weibo, Qzone, and so on, which also reflects its social characteristics. The obvious differences between communities allow users to quickly and accurately find the right community for them based on their interests and to get the information they need from the community members. This is the biggest benefit that a social commerce platform like Mogu brings to its users. Moreover, as we can see from the previous study, Mogu has carefully categorized users’ interests to satisfy different interest groups as much as possible.Although the connection between the members of the community in Mogu is relatively close, the connection between the community is relatively small, which also reflects the lack of development of most of the social commerce platforms like Mogu in China. As a representative of domestic social commerce platforms, the development of Mogu has its own characteristics. First, the entrance of Mogu users is mainly Taobao, Baidu, Weibo, and social networking sites. Second, the core of social commerce development is a common interest, and the source of development is driven by some commonality in users themselves and their temperament. Although the characteristics of each community in Mogu are more prominent, the connection between communities is not close enough, which leads to the inability to effectively use user resources within the whole platform. Third, the source of commodity information is relatively single, most of the commodity purchase links in Mogu are from Taobao, and there is no good connection with other shopping sites, which also leads to the homogenization of information on the platform which is more serious.


## 4. Conclusions

For precisely detecting user interests, clustering people, and conducting word-of-mouth marketing, the creation of a user interest graph and the finding of e-commerce communities are critical. The findings of this study can be used to provide a referential idea for how to design a social commerce user interest graph on the one hand and to make suggestions for merchants, platforms, and users that are in accordance with their own development on the other hand.


### 4.1. Countermeasure Suggestions for Merchants

The construction of social commerce user interest graph and their community discovery are important tools for merchants to realize accurate marketing and improve marketing efficiency. On the one hand, the wide variety of interests contained in the interest graph provides merchants with a source of information to promote products for different interests; on the other hand, online communities composed of the same interests have a high density of internal connections and are likely to generate several “opinion leaders,” so merchants can focus their marketing on them to improve efficiency. Merchants can do this in the following three ways: (1) seize the main interests to achieve precise marketing; (2) explore opinion leaders to realize e-commerce community; (3) play social attributes to achieve traffic multiplication.


### 4.2. Countermeasure Suggestions for the Platform

The construction of interest graphs and their community discovery are of great importance for e-commerce platforms. On the one hand, it can provide advertising placement efficiency; for both e-commerce platforms and social media platforms, bidding advertising has been the main way of their profit. A social commerce platform built on user interest mapping can not only improve the accuracy of its advertising but also increase the conversion rate from advertising effects to actual purchases. On the other hand, it can realize the sharing of user traffic between social and e-commerce platforms. In order to realize the enhancement of user wandering, the platform can be carried out in the following four aspects: (1) play the strong interactive, interactive, and interest consistency characteristics of the platform; (2) improve user experience and increase the import rate of customer traffic; (3) improve the interest tagging mechanism of the platform; (4) pay attention to the role of interest communities.


### 4.3. Countermeasure Suggestions for Users

For ordinary users, social commerce not only satisfies users’ shopping needs but also achieves the purpose of socializing with people. But to make better use of social commerce platforms to enhance their experience and avoid unnecessary product recommendations and spam, users need to (1) improve personal interest tags; (2) focus on privacy protection; (3) effectively use the platform’s user experience program.Social commerce is currently on the rise, although the research work in this paper provides a detailed description of the development and form of social commerce and focuses on one of the most representative forms. However, there are still many shortcomings in the theoretical scope of application and the practical process of experimental results that need to be improved and perfected. The following two aspects need to be considered in future research. One is to enrich the research object, expand the sample sources of the study, and conduct the mining of social commerce users’ interest graph and the discovery of online communities through comparative studies. This ensures the authenticity of the research process and the extensiveness and objectivity of the research data sources and also makes the results of the study applicable to different types of social e-commerce platforms. Secondly, the problem of user interest drift is considered. Interest graph is the basis of social commerce model development, and the interest graph constructed in this paper is based on the static formal description of users’ interests. However, in the process of daily interaction, users’ interests will change over time (i.e., user interest drift). If the user interest model is not updated in time when the interest changes, the performance of the constructed user interest graph will be degraded, which in turn affects the effectiveness of online community discovery. Therefore, how to establish an effective update mechanism to cope with the user interest drift becomes the difficulty and focus of the next work.


## 4.1. Countermeasure Suggestions for Merchants

The construction of social commerce user interest graph and their community discovery are important tools for merchants to realize accurate marketing and improve marketing efficiency. On the one hand, the wide variety of interests contained in the interest graph provides merchants with a source of information to promote products for different interests; on the other hand, online communities composed of the same interests have a high density of internal connections and are likely to generate several “opinion leaders,” so merchants can focus their marketing on them to improve efficiency. Merchants can do this in the following three ways: (1) seize the main interests to achieve precise marketing; (2) explore opinion leaders to realize e-commerce community; (3) play social attributes to achieve traffic multiplication.


## 4.2. Countermeasure Suggestions for the Platform

The construction of interest graphs and their community discovery are of great importance for e-commerce platforms. On the one hand, it can provide advertising placement efficiency; for both e-commerce platforms and social media platforms, bidding advertising has been the main way of their profit. A social commerce platform built on user interest mapping can not only improve the accuracy of its advertising but also increase the conversion rate from advertising effects to actual purchases. On the other hand, it can realize the sharing of user traffic between social and e-commerce platforms. In order to realize the enhancement of user wandering, the platform can be carried out in the following four aspects: (1) play the strong interactive, interactive, and interest consistency characteristics of the platform; (2) improve user experience and increase the import rate of customer traffic; (3) improve the interest tagging mechanism of the platform; (4) pay attention to the role of interest communities.


## 4.3. Countermeasure Suggestions for Users

For ordinary users, social commerce not only satisfies users’ shopping needs but also achieves the purpose of socializing with people. But to make better use of social commerce platforms to enhance their experience and avoid unnecessary product recommendations and spam, users need to (1) improve personal interest tags; (2) focus on privacy protection; (3) effectively use the platform’s user experience program.Social commerce is currently on the rise, although the research work in this paper provides a detailed description of the development and form of social commerce and focuses on one of the most representative forms. However, there are still many shortcomings in the theoretical scope of application and the practical process of experimental results that need to be improved and perfected. The following two aspects need to be considered in future research. One is to enrich the research object, expand the sample sources of the study, and conduct the mining of social commerce users’ interest graph and the discovery of online communities through comparative studies. This ensures the authenticity of the research process and the extensiveness and objectivity of the research data sources and also makes the results of the study applicable to different types of social e-commerce platforms. Secondly, the problem of user interest drift is considered. Interest graph is the basis of social commerce model development, and the interest graph constructed in this paper is based on the static formal description of users’ interests. However, in the process of daily interaction, users’ interests will change over time (i.e., user interest drift). If the user interest model is not updated in time when the interest changes, the performance of the constructed user interest graph will be degraded, which in turn affects the effectiveness of online community discovery. Therefore, how to establish an effective update mechanism to cope with the user interest drift becomes the difficulty and focus of the next work.



---

*Source: 7353151-2022-03-29.xml*