Exploring Social Annotations for Information Retrieval
Social annotation has gained increasing popularity in many Web-based applications, leading to an emerging research area in text analysis and information retrieval. This pa-per is concerned with developing probabilistic models and computational algorithms for social annotations. We pro-pose a unified framework to combine the modeling of social annotations with the language modeling-based methods for information retrieval. The proposed approach consists of two steps: (1) discovering topics in the contents and annota-tions of documents while categorizing the users by domains; and (2) enhancing document and query language models by incorporating user domain interests as well as topical back-ground models. In particular, we propose a new general generative model for social annotations, which is then sim-plified to a computationally tractable hierarchical Bayesian network. Then we apply smoothing techniques in a risk min-imization framework to incorporate the topical information to language models. Experiments are carried out on a real-world annotation data set sampled from del.icio.us. Our re-sults demonstrate significant improvements over traditional approaches.
social annotations folksonomy information retrieval language modeling
Ding Zhou Jiang Bian Shuyi Zheng Hongyuan Zha C. Lee Giles
Facebook Inc.156 University Avenue Palo Alto, CA, 94301 College of Computing Georgia Institute of Technology Atlanta, GA 30332 Computer Science & Engineering Pennsylvania State University University Park, PA 16802 Information Sciences andTechnology Computer Science & Engineering Pennsylvania State University Univ
国际会议
第十七届国际万维网大会(the 17th International World Wide Web Conference)(WWW08)
北京
英文
2008-04-21（万方平台首次上网日期，不代表论文的发表时间）