Query Recommendation Using Large-Scale Web Access Logs and Web Page Archive

Query recommendation suggests related queries for search engine users when they are not satisfied with the results of an initial input query, thus assisting users in improving search quality. Conventional approaches to query recommendation have been focused on expanding a query by terms extracted from various information sources such as a thesaurus like WordNet, the top ranked documents and so on. In this paper, we argue that past queries stored in query logs can be a source of additional evidence to help future users. We present a query recommendation system based on large-scale Web access logs and Web page archive, and evaluate three query recommendation strategies based on different feature spaces (i.e., noun, URL, and Web community). The experimental results show that query logs are an effective source for query recommendation, and the Web community-based and noun-based strategies can extract more related search queries than the URL-based one.