Dynamic query expansion based on user's real time implicit feedback

Majority of the queries submitted to search engines are short and under-specified. Query expansion is a commonly used technique to address this issue. However, existing query expansion frameworks have an inherent problem of poor coherence between expansion terms and user's search goal. User's search goal, even for the same query, may be different at different instances. This often leads to poor retrieval performance. In many instances, user's current search is influenced by his/her recent searches. In this paper, we study a framework which explores user's implicit feedback provided at the time of search to determine user's search context. We then incorporate the proposed framework with query expansion to identify relevant query expansion terms. From extensive experiments, it is evident that the proposed framework can capture the dynamics of user's search and adapt query expansion accordingly.