Modern information retrieval (IR) systems exhibit user dynamics through interactivity. These dynamic aspects of IR, including changes found in data, users, and systems, are increasingly being utilized in search engines. Session search is one such IR task—document retrieval within a session. During a session, a user constantly modifies queries to find documents that fulfill an information need. Existing IR techniques for assisting the user in this task are limited in their ability to optimize over changes, learn with a minimal computational footprint, and be responsive. This article proposes a novel query change retrieval model (QCM), which uses syntactic editing changes between consecutive queries, as well as the relationship between query changes and previously retrieved documents, to enhance session search. We propose modeling 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 engine agent’s actions are term weight adjustments as proposed in this work. We also investigate multiple query aggregation schemes and their effectiveness on session search. Experiments show that our approach is highly effective and outperforms top session search systems in TREC 2011 and TREC 2012.

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.

Search result clustering (SRC) is a post-retrieval process that hierarchically organizes search results. The hierarchical structure offers overview for the search results and displays an "information lay-of-land" that intents to guide the users throughout a search session. However, SRC hierarchies are sensitive to query changes, which are common among queries in the same session. This instability may leave users seemly random overviews throughout the session. We present a new tool called InfoLand that integrates external knowledge from Wikipedia when building SRC hierarchies and increase their stability. Evaluation on TREC 2010-2011 Session Tracks shows that InfoLand produces more stable results organization than a commercial search engine.

Session search is the Information Retrieval (IR) task that performs document retrieval for an entire session. During a session, users often change queries to explore and investigate the information needs. In this paper, we propose to use query change as a new form of relevance feedback for better session search. Evaluation conducted over TREC 2012 Session Track shows that query change is a highly effective form of feedback as compared with existing relevance feedback methods. The proposed method outperforms the state-of-the-art relevance feedback methods for the TREC 2012 Session Track by a significant improvement of >25%.