Agglomerative clustering of a search engine query log
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Clustering user queries of a search engine
Proceedings of the 10th international conference on World Wide Web
Journal of the American Society for Information Science and Technology
Eye-tracking analysis of user behavior in WWW search
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Generating query substitutions
Proceedings of the 15th international conference on World Wide Web
Mining search engine query logs for query recommendation
Proceedings of the 15th international conference on World Wide Web
An experimental comparison of click position-bias models
WSDM '08 Proceedings of the 2008 International Conference on Web Search and Data Mining
Query suggestion using hitting time
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
Learning latent semantic relations from clickthrough data for query suggestion
Proceedings of the 17th ACM conference on Information and knowledge management
A dynamic bayesian network click model for web search ranking
Proceedings of the 18th international conference on World wide web
Query-URL bipartite based approach to personalized query recommendation
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
An optimization framework for query recommendation
Proceedings of the third ACM international conference on Web search and data mining
A model to estimate intrinsic document relevance from the clickthrough logs of a web search engine
Proceedings of the third ACM international conference on Web search and data mining
A structured approach to query recommendation with social annotation data
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
A unified framework for recommending diverse and relevant queries
Proceedings of the 20th international conference on World wide web
Synthesizing high utility suggestions for rare web search queries
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Proceedings of the 20th ACM international conference on Information and knowledge management
Context-aware query recommendation by learning high-order relation in query logs
Proceedings of the 20th ACM international conference on Information and knowledge management
Recommending high utility query via session-flow graph
ECIR'13 Proceedings of the 35th European conference on Advances in Information Retrieval
User model-based metrics for offline query suggestion evaluation
Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval
Heterogeneous graph-based intent learning with queries, web pages and Wikipedia concepts
Proceedings of the 7th ACM international conference on Web search and data mining
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Query recommendation plays a critical role in helping users' search. Most existing approaches on query recommendation aim to recommend relevant queries. However, the ultimate goal of query recommendation is to assist users to reformulate queries so that they can accomplish their search task successfully and quickly. Only considering relevance in query recommendation is apparently not directly toward this goal. In this paper, we argue that it is more important to directly recommend queries with high utility, i.e., queries that can better satisfy users' information needs. For this purpose, we propose a novel generative model, referred to as Query Utility Model (QUM), to capture query utility by simultaneously modeling users' reformulation and click behaviors. The experimental results on a publicly released query log show that, our approach is more effective in helping users find relevant search results and thus satisfying their information needs.