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

@inproceedings{cao_2008_context, abstract = {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 offine model-learning 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 sufix 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.}, address = {New York, NY, USA}, author = {Cao, Huanhuan and Jiang, Daxin and Pei, Jian and He, Qi and Liao, Zhen and Chen, Enhong and Li, Hang}, booktitle = {KDD '08: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining}, citeulike-article-id = {3376452}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1401890.1401995}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1401890.1401995}, comment = {Cao {\em et al.}\~{}\cite{cao\_2008\_context} map the recent search history of a user from a sequence of queries to a sequence of concepts, in which concepts are obtained by clustering a query log. Then, a suffix from this sequence of concepts is matched to known sequence fragments in the query log to generate a recommendation. }, doi = {10.1145/1401890.1401995}, isbn = {978-1-60558-193-4}, keywords = {clicks, clustering, queries, sequence}, location = {Las Vegas, Nevada, USA}, pages = {875--883}, posted-at = {2009-01-26 10:09:46}, priority = {0}, publisher = {ACM}, title = {Context-aware query suggestion by mining click-through and session data}, url = {http://dx.doi.org/10.1145/1401890.1401995}, year = {2008} }

TY - CONF ID - cao_2008_context L3 - citeulike-article-id:3376452 N2 - 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 offine model-learning 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 sufix 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. CY - Las Vegas, Nevada, USA SN - 978-1-60558-193-4 AD - New York, NY, USA EP - 883 TI - Context-aware query suggestion by mining click-through and session data PB - ACM T2 - KDD '08: Proceeding of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining SP - 875 KW - clicks KW - clustering KW - queries KW - sequence N1 - Cao {\em et al.}~\cite{cao_2008_context} map the recent search history of a user from a sequence of queries to a sequence of concepts, in which concepts are obtained by clustering a query log. Then, a suffix from this sequence of concepts is matched to known sequence fragments in the query log to generate a recommendation. AU - Cao, Huanhuan AU - Jiang, Daxin AU - Pei, Jian AU - He, Qi AU - Liao, Zhen AU - Chen, Enhong AU - Li, Hang PY - 2008/// UR - http://dx.doi.org/10.1145/1401890.1401995 ER -