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.
exploration social annotations annotation information retrieval informal web-based applications research text analysis paper model computation algorithm language modeling language modeling method contention documentation categorical user user query languages general generation model hierarchic bayesian network smooth risk minimal experience data demonstrate  framework  folksonomy users language model