Document language models, query models, and risk minimization for information retrieval
Proceedings of the 24th annual international ACM SIGIR conference on Research and development in information retrieval
Model-based feedback in the language modeling approach to information retrieval
Proceedings of the tenth international conference on Information and knowledge management
Probabilistic query expansion using query logs
Proceedings of the 11th international conference on World Wide Web
Optimizing search engines using clickthrough data
Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining
The Journal of Machine Learning Research
Parsimonious language models for information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
InfoScale '06 Proceedings of the 1st international conference on Scalable information systems
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User click-throughs provide a search context for understanding the user need of complex information. This paper re-examines the effectiveness of this approach when based on partial clicked data using the language modeling framework. We expand the original query by topical terms derived from clicked Web pages and enhance early precision via a more compact document representation. Since our URLs of Web pages are stripped, we first reconstruct them at different levels based on different collections. Our experimental results on the GOV2 test collection and AOL query log show improvement by 31.7% and 28.3% significantly in statMAP for two sources of reconstruction and 153 ad-hoc queries. Our model also outperforms pseudo relevance feedback.