Exploring social annotations for information retrieval

Ding Zhou, Jiang Bian, Shuyi Zheng, Hongyuan Zha, C. Lee Giles
2008 Proceeding of the 17th international conference on World Wide Web - WWW '08  
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
more » ... ing 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 realworld annotation data set sampled from del.icio.us. Our results demonstrate significant improvements over traditional approaches.
doi:10.1145/1367497.1367594 dblp:conf/www/ZhouBZZG08 fatcat:xbbsr6fcpfekdaunrq7mw4vxvu