Query expansion using local and global document analysis
SIGIR '96 Proceedings of the 19th annual international ACM SIGIR conference on Research and development in information retrieval
Agglomerative clustering of a search engine query log
Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Query clustering using content words and user feedback
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
Query Expansion by Mining User Logs
IEEE Transactions on Knowledge and Data Engineering
WISE '00 Proceedings of the First International Conference on Web Information Systems Engineering (WISE'00)-Volume 1 - Volume 1
The Journal of Machine Learning Research
Understanding user goals in web search
Proceedings of the 13th international conference on World Wide Web
Optimizing web search using web click-through data
Proceedings of the thirteenth ACM international conference on Information and knowledge management
Query chains: learning to rank from implicit feedback
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
InfoScale '06 Proceedings of the 1st international conference on Scalable information systems
LDA-based document models for ad-hoc retrieval
SIGIR '06 Proceedings of the 29th annual international ACM SIGIR conference on Research and development in information retrieval
Exploring social annotations for information retrieval
Proceedings of the 17th international conference on World Wide Web
Content-Based Clustering for Tag Cloud Visualization
ASONAM '09 Proceedings of the 2009 International Conference on Advances in Social Network Analysis and Mining
Context-aware ranking in web search
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
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It is non-trivial to formulate a query that can precisely describe the goal of an informational search task. Query reformulation based on the query clustering approach addresses this issue by expanding a new query with related existing queries that were generated by other users. However, the query clustering approach is unable to cluster queries that are intrinsically related but neither contain common terms nor return common clicked Web page URLs. More importantly, it does not address the issue of ranking retrieved results according to their relevance to the search goal. In this paper, we present new query reformulation approach based on a novel probabilistic topic model to discovering the latent semantic relationships between the queries and the URLs. It can not only discover related queries that cannot be clustered by existing query clustering approaches but also rank retrieved results according to the similarities of probability distributions over the latent topics among the queries and the URLs. The results of our experiments have shown that this approach can significantly improve the performance of an informational search task in terms of search accuracy and search efficiency.