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SIGIR
2009
ACM

Predicting user interests from contextual information

12 years 1 days ago
Predicting user interests from contextual information
Search and recommendation systems must include contextual information to effectively model users’ interests. In this paper, we present a systematic study of the effectiveness of five variant sources of contextual information for user interest modeling. Postquery navigation and general browsing behaviors far outweigh direct search engine interaction as an information-gathering activity. Therefore we 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. We evaluate the utility of these sources, and overlaps between them, based on how effectively they predict users’ future interests. Our findings demonstrate that the sources perform differently depending on the duration of the time window used for future prediction, and that context overlap outperforms any isolated source. Designers of Website suggestion systems can use our findings to provide...
Ryen W. White, Peter Bailey, Liwei Chen
Added 28 May 2010
Updated 28 May 2010
Type Conference
Year 2009
Where SIGIR
Authors Ryen W. White, Peter Bailey, Liwei Chen
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