Predicting User Interests From Contextual Information
Search and recommendation systems must include contextual information to effectively model users' interests. This paper presents a systematic study of the effectiveness of five variant sources of contextual information for user interest modeling. Post-query navigation and general browsing behaviors far outweigh direct search engine interaction as an information-gathering activity. Therefore the paper conducted this study with a focus on Website recommendations rather than search results. The five contextual information sources used are: social, historic, task, collection, and user interaction. The paper evaluates the utility of these sources, and overlaps between them, based on how effectively they predict users' future interests.