Queries to search engines on the Web are usually short. They do not provide sufficient evidence for an effective selection of relevant documents. Previous research has proposed the utilization of query expansion to deal with this problem. However, expansion terms are usually determined on term co-occurrences within documents. In this study, we propose a new method for query expansion based on user interactions recorded in user logs. The central idea is to extract correlations between query terms and document terms by analyzing user logs. These correlations are then used to select high-quality expansion terms for new queries. Compared to

Queries to search engines on the Web are usually short. They do not provide sufficient indications for an effective selection of relevant documents. Previous research has proposed the utilization of query expansion to deal with this problem. However, expansion terms are determined only on the analysis of documents. In this study, we propose a new method for query expansion based on user interaction information recorded in the web query logs. The central idea is to extract correlations between query terms and document terms by analyzing query logs. These correlations are then used to select high-quality expansion terms for new queries. In comparison with previous query expansion method, our method takes advantage of the user judgments implied in user logs. Our experimental results show that the log-based query expansion method can produce much better results than both the classical search method and the other query expansion methods.