Analysis of a very large web search engine query log
ACM SIGIR Forum
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
Automatic new topic identification using multiple linear regression
Information Processing and Management: an International Journal
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Journal of the American Society for Information Science and Technology
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Proceedings of the 17th ACM conference on Information and knowledge management
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BROADCOM '08 Proceedings of the 2008 Third International Conference on Broadband Communications, Information Technology & Biomedical Applications
A survey on session detection methods in query logs and a proposal for future evaluation
Information Sciences: an International Journal
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IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
Markovian analysis for automatic new topic identification in search engine transaction logs
Applied Stochastic Models in Business and Industry
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In this paper, we describe a SVM classification framework of session detection task on both Chinese and English query logs. With eight features on the aspects of temporal and content information extracted from pairs of successive queries, the classification models achieve significantly superior performance than the stat-of-the-art method. Additionally, we find through ROC analysis that there exists great discrimination power variability among different features and within the same feature across different users. To fully utilize this variability, we build local models for individual users and combine their predictions with those from the global model. Experiments show that the local models do make significant improvements to the global model, although the amount is small.