Utilizing query change for session search

Abstract

Session search is the Information Retrieval (IR) task that performs document retrieval for a search session. During a session, a user constantly modifies queries in order to find relevant documents that fulfill the information need. This paper proposes a novel query change retrieval model (QCM), which utilizes syntactic editing changes between adjacent queries as well as the relationship between query change and previously retrieved documents to enhance session search. We propose to model session search as a Markov Decision Process (MDP). We consider two agents in this MDP: the user agent and the search engine agent. The user agent's actions are query changes that we observe and the search agent's actions are proposed in this paper. Experiments show that our approach is highly effective and outperforms top session search systems in TREC 2011 and 2012.