Abstract
Interactive web search involves selecting which documents to read further and locating the parts of the documents that are relevant to the user's current activity. In this paper, we introduce UIMaP: User Interest Modeling and Personalization, a search task based personal user interest model to support users' information gathering tasks. The novelty of our approach lies in the use of topic modeling to generate fine-grained models of user interest and visualizations that direct user's attention to documents or parts of documents that match user's inferred interests. User annotations are used to help generate personalized visualizations for user's search tasks. Based on 1267 user annotations from 17 users, we show the performance comparisons of four different topic models: LDA+H, LDA+KL, LDA+JSD, and LDA+TopN.
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