关键词: 微博, 相似用户, 兴趣话题, 文本训练, 用户挖掘

Abstract: Similar user mining is an important way to improve the quality of social network services. In the era of big data-oriented social networks, accurate similar user mining has important meanings for users and Internet companies. Similar users mined from users’ interest topics are more consistent with similar users’ requirements. This paper proposes a method for similar user mining based on user interest topics. The method first uses the TextRank topic extraction method to extract the interest topics of the user, and then trains users’ content to calculate the similarity between all the words. Four methods for calculating similarity of user interest topic words, such as CP（Corresponding Position similarity）, CPW（Corresponding Position Weighted similarity）, AP（All Position similarity）, and APW（All Position Weighted similarity）, are proposed. The coincidence rate verifies the similar user mining effect. The similar user’s followers/fans coincidence percentage of APW similarity is 1.687%, which is better than the other three algorithms proposed, which are increased by 26.3%, 2.8%, 12.4%. Meanwhile, the coincidence rate of proposed method is better than the traditional text similarity methods and Jaccard similarity, edit distance algorithm, and cosine similarity are improved by 20.4%, 21.2%, and 45.0%, respectively. Therefore, the APW method can more effectively mine similar users of users.

Key words: Weibo, similar users, interest topic, text training, user mining