Probabilistic query expansion using query logs
Proceedings of the 11th international conference on World Wide Web
Combining evidence for automatic web session identification
Information Processing and Management: an International Journal - Issues of context in information retrieval
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
The Journal of Machine Learning Research
Query expansion using associated queries
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Mining anchor text for query refinement
Proceedings of the 13th international conference on World Wide Web
Query chains: learning to rank from implicit feedback
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Generating query substitutions
Proceedings of the 15th international conference on World Wide Web
InfoScale '06 Proceedings of the 1st international conference on Scalable information systems
Improving web search ranking by incorporating user behavior information
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Combining fields for query expansion and adaptive query expansion
Information Processing and Management: an International Journal
Personalized query expansion for the web
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Studying the use of popular destinations to enhance web search interaction
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Context sensitive stemming for web search
SIGIR '07 Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval
Effective and efficient user interaction for long queries
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
A unified and discriminative model for query refinement
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Analyzing web text association to disambiguate abbreviation in queries
Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Context-aware query suggestion by mining click-through and session data
Proceedings of the 14th ACM SIGKDD international conference on Knowledge discovery and data mining
Topical N-Grams: Phrase and Topic Discovery, with an Application to Information Retrieval
ICDM '07 Proceedings of the 2007 Seventh IEEE International Conference on Data Mining
Understanding the relationship between searchers' queries and information goals
Proceedings of the 17th ACM conference on Information and knowledge management
Query suggestion using hitting time
Proceedings of the 17th ACM conference on Information and knowledge management
Mining term association patterns from search logs for effective query reformulation
Proceedings of the 17th ACM conference on Information and knowledge management
The query-flow graph: model and applications
Proceedings of the 17th ACM conference on Information and knowledge management
Query suggestions using query-flow graphs
Proceedings of the 2009 workshop on Web Search Click Data
Entropy-biased models for query representation on the click graph
Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval
Analyzing and evaluating query reformulation strategies in web search logs
Proceedings of the 18th ACM conference on Information and knowledge management
Query reformulation using anchor text
Proceedings of the third ACM international conference on Web search and data mining
Clustering query refinements by user intent
Proceedings of the 19th international conference on World wide web
A structured approach to query recommendation with social annotation data
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Personalized search by tag-based user profile and resource profile in collaborative tagging systems
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Improving verbose queries using subset distribution
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Clickthrough-based translation models for web search: from word models to phrase models
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
Query recommendation for children
Proceedings of the 21st ACM international conference on Information and knowledge management
Contextual evaluation of query reformulations in a search session by user simulation
Proceedings of the 21st ACM international conference on Information and knowledge management
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An important way to improve users' satisfaction in Web search is to assist them to issue more effective queries. One such approach is query refinement (reformulation), which generates new queries according to the current query issued by users. A common procedure for conducting refinement is to generate some candidate queries first, and then a scoring method is designed to assess the quality of these candidates. Currently, most of the existing methods are context based. They rely heavily on the context relation of terms in the historical queries, and cannot detect and maintain the semantic consistency of queries. In this paper, we propose a graphical model to score queries. The proposed model exploits a latent topic space, which is automatically derived from the query log, to assess the semantic dependency of terms in a query. In the graphical model, both term context dependency and topic context dependency are considered. This also makes it feasible to score some queries which do not have much available historical term context information. We also utilize social tagging data in the candidate query generation process. Based on the observation that different users may tag the same resource with different tags of similar meaning, we propose a method to mine these term pairs for new candidate query construction.