Combining evidence for automatic web session identification
Information Processing and Management: an International Journal - Issues of context in information retrieval
Query chains: learning to rank from implicit feedback
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Implicit user modeling for personalized search
Proceedings of the 14th ACM international conference on Information and knowledge management
InfoScale '06 Proceedings of the 1st international conference on Scalable information systems
The query-flow graph: model and applications
Proceedings of the 17th ACM conference on Information and knowledge management
Beyond the session timeout: automatic hierarchical segmentation of search topics in query logs
Proceedings of the 17th ACM conference on Information and knowledge management
A survey on session detection methods in query logs and a proposal for future evaluation
Information Sciences: an International Journal
Computing semantic relatedness using Wikipedia-based explicit semantic analysis
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Models of searching and browsing: languages, studies, and applications
IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Identifying task-based sessions in search engine query logs
Proceedings of the fourth ACM international conference on Web search and data mining
A Hidden Topic-Based Framework toward Building Applications with Short Web Documents
IEEE Transactions on Knowledge and Data Engineering
Query session detection as a cascade
Proceedings of the 20th ACM international conference on Information and knowledge management
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To accomplish a search task and satisfy a single information need, users usually submit a series of queries to web search engines. It is useful for web search engines to detect the task boundaries in a series of successive queries. Traditional task boundary detection methods are based on time gap and lexical comparisons, which often suffer from the vocabulary gap problem, that is, the topically related queries may not share any common words. In this paper we learn hidden topics from query log and leverage them to resolve the vocabulary gap problem. Unlike other external knowledge resources, such as WordNet and Wikipedia, the hidden topics discovered from query log cover long tail queries, which is useful to detect task boundaries. Experimental results on dataset from real world query log demonstrate that the proposed method achieves significant quality enhancement.