Exploring social annotations for information retrieval

  • Authors:

  • Ding Zhou;Jiang Bian;Shuyi Zheng;Hongyuan Zha;C. Lee Giles

  • Affiliations:

  • Facebook Inc., Palo Alto, CA, USA;Georgia Institute of Technology, Atlanta, GA, USA;The Pennsylvania State University, University Park, PA, USA;Georgia Institute of Technology, Atlanta, GA, USA;The Pennsylvania State University, University Park, PA, USA

  • Venue:
  • Proceedings of the 17th international conference on World Wide Web
  • Year:
  • 2008

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Abstract

Social annotation has gained increasing popularity in many Web-based applications, leading to an emerging research area in text analysis and information retrieval. This paper is concerned with developing probabilistic models and computational algorithms for social annotations. We propose 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 annotations 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 background models. In particular, we propose a new general generative model for social annotations, which is then simplified to a computationally tractable hierarchical Bayesian network. Then we apply smoothing techniques in a risk minimization 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 results demonstrate significant improvements over the traditional approaches.