document: 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 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 .
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