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
A study of smoothing methods for language models applied to Ad Hoc 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
Context-sensitive information retrieval using implicit feedback
Proceedings of the 28th annual international ACM SIGIR conference on Research and development in information retrieval
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
Implicit user modeling for personalized search
Proceedings of the 14th ACM international conference on Information and knowledge management
Mining long-term search history to improve search accuracy
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Learning to rank from relevance feedback for e-discovery
ECIR'12 Proceedings of the 34th European conference on Advances in Information Retrieval
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Hard queries are known to benefit from relevance feedback provided by users. It is, however, also known that users are generally reluctant to provide feedback when searching for information. A natural resort not demanding any active user participation is to exploit implicit feedback from the previous user search behavior, i.e., from the context of the current search session. In this work, we present a comparative study on the performance of the three most prominent retrieval models, the vector-space, probabilistic, and language-model based retrieval frameworks, when additional session context is incorporated.