Predicting short-term interests using activity-based search context

A query considered in isolation offers limited information about a searcher's intent. Query context that considers pre-query activity (e.g., previous queries and page visits), can provide richer information about search intentions. In this paper, we describe a study in which we developed and evaluated user interest models for the current query, its context (from pre-query session activity), and their combination, which we refer to as intent. Using large-scale logs, we evaluate how accurately each model predicts the user's short-term interests under various experimental conditions. In our study we: (i) determine the extent of opportunity for using context to model intent; (ii) compare the utility of different sources of behavioral evidence (queries, search result clicks, and Web page visits) for building predictive interest models, and; (iii) investigate optimally combining the query and its context by learning a model that predicts the context weight for each query. Our find...

• Type: Journal
• Year: 2010
• Where: CIKM
• Authors: Ryen W. White, Paul N. Bennett, Susan T. Dumais
• Keywords: CIKM 2010, Contextual Information, Information, Information Technology, Short-term Interests