Modeling user´s preference in folksonomy for personalized search

Personalized search based on the users´ preference has been extensively studied in the field of information retrieval. As a typical representative of web2.0, social tagging not only allows users to better describe and manage web resources, but also provides a great opportunity for the personalized search research, since it contains abundant public personal information. In this paper we propose a user preferences model based on tag clustering. This model is inspired by the Data Field theory. It can depict users´ different aspects of interests and dynamically generate users´ preferences against different search queries. We apply this model in the data set of flickr. The final ranked pictures are the combination of keywords and users´ preference matching. The experiment proves that our method is better than both non-personalization method and common personalization method.