Clustering user queries of a search engine
Proceedings of the 10th international conference on World Wide Web
Query association for effective retrieval
Proceedings of the eleventh international conference on Information and knowledge management
Finding Similar Queries to Satisfy Searches Based on Query Traces
OOIS '02 Proceedings of the Workshops on Advances in Object-Oriented Information Systems
Query expansion using associated queries
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Query recommendation using query logs in search engines
EDBT'04 Proceedings of the 2004 international conference on Current Trends in Database Technology
Using related queries to improve web search results ranking
SPIRE'10 Proceedings of the 17th international conference on String processing and information retrieval
Click-graph modeling for facet attribute estimation of web search queries
RIAO '10 Adaptivity, Personalization and Fusion of Heterogeneous Information
A statistical model of query log generation
SPIRE'06 Proceedings of the 13th international conference on String Processing and Information Retrieval
Semantics of query rewriting patterns in search logs
Proceedings of the fifth workshop on Exploiting semantic annotations in information retrieval
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We present a method to help a user redefine a query based on past users experience, namely the click-through data as recorded by a search engine. Unlike most previous works, the method we propose attempts to recommend better queries rather than related queries. It is effective at identifying query specialization or sub-topics because it take into account the co-occurrence of documents in individual query sessions. It is also particularly simple to implement.