## 3 User Similarity Measurement for Short Text
### Multidimensional Feature Analysis of Microblog Users  
Microblog user information is mainly divided into three categories: ID label information used to uniquely identify user identities; basic information that users can fill in when they register their accounts, which include descriptions of birthdays, age, gender, labels, etc.; users' data information of activities on the Microblog platform, such as number of followees, number of followers, Microblog number, forwarding amount, etc. Labels are the most direct summaries of personal personality traits, hobbies, and areas of expertise [1; 2; 3]. The number of followees, followers, and blog posts can indirectly reflect the impact and active degree of users. Based on the user's information search motivation, information sharing motivation, and other factors, a Microblog user's postings, followings, forwarding, comments, '@' and other active behavior characteristics are formed.  
Microblog users relationship mining mainly includes two aspects: the community that discovers the user relationship structure; and the core user identification that measures user influence. This paper investigates the Microblog user community discovery method. At present, the clustering methods of Microblog users are mainly based on the user's basic attributes (age, gender, educational level, place of birth), and cannot reveal the user group characteristics based on text information. User information and text information are used as the two most important entities on the Microblog platform, which stores massive amounts of structured and unstructured data. Therefore, a new user interest model is constructed by incorporating text comment information in this paper.  
This paper takes Microblog, released by the Xiaomi Company, as an example. The company collects feedback from users and obtains 1200 fans' effective evaluations of the use of Xiaomi phones. At the same time, the other basic information of these users can be used. The interest model of the Microblog user to the product is constructed from three dimensions such as user attributes, user behavior, and text features. Researchers have found that consumers of different genders have different needs and information processing methods, which lead to different brand awareness and consumption behaviors [16]. Labels are the keywords that most directly show user interest. Therefore, the two attributes of gender and label are important factors in showing user interest. The weak contact between ordinary users is mainly based on common follows. The 'follow' in this article refers to the user's concern for the relevant influencer account in a specific field. We selected 166 brand-related influencer accounts from the list of the top 100 followers of popular comments. There are three main categories: enterprise-related accounts, including Xiaomi derivative brand accounts and Xiaomi company founders; digital bloggers, including digital product evaluation accounts and new products and industry information sharing accounts; commodity accounts, mainly the official Microblog account of other mobile phones of the same type. The user comments under the official Microblog of the enterprise include the use of experience, emotional tendency, etc. And the relevance to the brand products is high. Product comments mainly include several categories: product brand awareness, product buying experience, product using experience, hot brand topics discussion, brand activity participation, etc. Users' comment information is unstructured data, which need to be converted into structured data for computer processing.  
A vector space model is then used to represent the text set as a vector set consisting of the feature word dimensions and the corresponding weights in the corresponding space. Table 1 shows the classification of the key feature words of user comments.