A new query expansion method based on query logs mining

Query expansion has long been suggested as an effective way to improve the performance of information retrieval systems by adding additional relevant terms to the original queries. However, most previous research has been limited in extracting new terms from a subset of relevant documents, but has not exploited the information about user interactions. In this paper, we proposed a method for automatic query expansion based on user interactions recorded in query logs. The central idea is to extract correlations among queries by analyzing the common documents the users selected for them, and the expanded terms only come from the associated queries more than the relevant documents. In particular, we argue that queries should be dealt with in different ways according to their ambiguity degrees, which can be calculated from the log information. We verify this method in a large scale query logs collection and the experimental results show that the method makes good use of the knowledge of user interactions, and it can remarkably improve search performance.