LDA-based User Interests Discovery in Collaborative Tagging System

摘要：

The success and popularity of collaborative tagging systems, such as del.ici.ous1, Flickr2, last.fm3, has increasingly centered on. Users of these websites can easily tag their interested WebPages, photos and music with their preferred words. Subsequently, the extensive tagging data attract many researchers to mine useful information from these. In this paper, we propose a novel user interests quantified approach based on user-generated tags. Moreover, by means of the generative probabilistic model Latent Dirichlet Allocation (LDA), we acquire the interests for each user. Experimenting with the dataset provided within the ECML PKDD Discovery Challenge 2009, our method makes better performance.

作者: Shuang Song Li Yu Xiaoping Yang

作者单位: Information School, Renmin University of China Beijing, China

会议类型: 国际会议

会议名称: The 2010 International Conference on Intelligent Systems and Knowledge Engineering(第五届智能系统与知识工程国际会议)

会议地点: 杭州

会议语种:英文

页码: 338-343

在线出版日期: 2010-11-15（万方平台首次上网日期，不代表论文的发表时间）