Foundations of statistical natural language processing
Foundations of statistical natural language processing
Analysis of a very large web search engine query log
ACM SIGIR Forum
Transparent Queries: investigation users' mental models of search engines
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Automatic query wefinement using lexical affinities with maximal information gain
SIGIR '02 Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval
Using terminological feedback for web search refinement: a log-based study
Proceedings of the 26th annual international ACM SIGIR conference on Research and development in informaion 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
Beyond the session timeout: automatic hierarchical segmentation of search topics in query logs
Proceedings of the 17th ACM conference on Information and knowledge management
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Previous studies reveal that half of the queries submitted to search engines have no follow-up click-through data. This may indicate that users are either dissatisfied with the performance of current search engines or have difficulty formulating correct query keywords related to their search intents. To address this issue, this paper proposes a query refinement mechanism called RebaCQ, which can help users obtain satisfactory pages as soon as possible. By reusing user personal wisdom extracted from their previous consecutive queries, RebaCQ can provide refined result sets closer to user intents. Our experimental results show that result accuracy is significantly increased after adapting RebaCQ.