Cumulated gain-based evaluation of IR techniques
ACM Transactions on Information Systems (TOIS)
CIKM '03 Proceedings of the twelfth international conference on Information and knowledge management
Query suggestion using hitting time
Proceedings of the 17th ACM conference on Information and knowledge management
Expected reciprocal rank for graded relevance
Proceedings of the 18th ACM conference on Information and knowledge management
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
Context-sensitive query auto-completion
Proceedings of the 20th international conference on World wide web
Structured query suggestion for specialization and parallel movement: effect on search behaviors
Proceedings of the 21st international conference on World Wide Web
Time-sensitive query auto-completion
SIGIR '12 Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval
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Most commercial search engines have a query suggestion feature, which is designed to capture various possible search intents behind the user's original query. However, even though different search intents behind a given query may have been popular at different time periods in the past, existing query suggestion methods neither utilize nor present such information. In this study, we propose Time-aware Structured Query Suggestion (TaSQS) which clusters query suggestions along a timeline so that the user can narrow down his search from a temporal point of view. Moreover, when a suggested query is clicked, TaSQS presents web pages from query-URL bipartite graphs after ranking them according to the click counts within a particular time period. Our experiments using data from a commercial search engine log show that the time-aware clustering and the time-aware document ranking features of TaSQS are both effective.