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 proposed 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 immediately preceding queries as context in query suggestion. In this paper, we propose a novel context-aware query suggestion approach which is in two steps. In the offline modellearning step, to address data sparseness, queries are summarized into concepts by clustering a click-through bipartite. 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 concept sequence suffix tree, our approach suggests queries to the user in a context-aware manner. We test our approach on a large-sc...

• Type: Conference
• Year: 2008
• Where: KDD
• Authors: Huanhuan Cao, Daxin Jiang, Jian Pei, Qi He, Zhen Liao, Enhong Chen, Hang Li
• Keywords: Context-aware Query Suggestion, Data Mining, KDD 2008, Online Query Suggestion, Query Suggestion Model