Some simple effective approximations to the 2-Poisson model for probabilistic weighted retrieval
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A language modeling approach to information retrieval
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Model-based feedback in the language modeling approach to information retrieval
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Introduction to the Special Issue: Overview of the TREC Routing and Filtering Tasks
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Implicit feedback for inferring user preference: a bibliography
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Personalizing search via automated analysis of interests and activities
Proceedings of the 28th annual international ACM SIGIR conference on Research and development 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
InfoScale '06 Proceedings of the 1st international conference on Scalable information systems
Improving web search ranking by incorporating user behavior information
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Learn from web search logs to organize search results
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Mining web query hierarchies from clickthrough data
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
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Modeling and analysis of cross-session search tasks
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
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A user's web search history contains many valuable search patterns. In this paper, we study search patterns that represent a user's long-lasting and exploratory search interests. By focusing on long-lastingness and exploratoriness, we are able to discover search patterns that are most useful for recommending new and relevant information to the user. Our approach is based on language modeling and clustering, and specifically designed to handle web search logs. We run our algorithm on a real web search log collection, and evaluate its performance using a novel simulated study on the same search log dataset. Experiment results support our hypothesis that long-lastingness and exploratoriness are necessary for generating successful recommendation. Our algorithm is shown to effectively discover such search interest patterns, and thus directly useful for making recommendation based on personal search history.