A session-oriented retrieval model based on Markov random field

In this paper, we study how to use the search session information to improve the retrieval accuracy. We propose a session-oriented retrieval model based on Markov random field. This model introduces the correlations between query terms as a retrieval factor into the retrieval process. It also presents a dynamic update algorithm based on the analysis of users´ search behavior. Our model implements a complete session-oriented information retrieval framework finally. We use ClueWeb09 category B dataset and TREC 2010 (2011) Session dataset to quantitatively evaluate the model. Experimental results show that our model can improve retrieval performance substantially using the search session information.