Context-aware query suggestion by mining click-through and session data

Query suggestion plays an important role in improving the usability of search engines. Although some recently pro-posed methods can make meaningful query suggestions by mining query patterns from search logs, none of them are context-aware – they do not take into account the immedi-ately preceding queries as context in query suggestion. In this paper, we propose a novel context-aware query sugges-tion approach which is in two steps. In the offline model-learning step, to address data sparseness, queries are sum-marized into concepts by clustering a click-through bipar-tite. Then, from session data a concept sequence suffix tree is constructed as the query suggestion model. In the online query suggestion step, a user’s search context is captured by mapping the query sequence submitted by the user to a sequence of concepts. By looking up the context in the con-cept sequence suffix tree, our approach suggests queries to the user in a context-aware manner. We test our approach on a large-scale search log of a commercial search engine containing 1.8 billion search queries, 2.6 billion clicks, and 840 million query sessions. The experimental results clearly show that our approach outperforms two baseline methods in both coverage and quality of suggestions