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Abstract

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 way to retrieve the most relevant information satisfying the user need without actually 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 \emph{vector-space}, \emph{probabilistic}, and \emph{language-model based} retrieval frameworks, when additional session context is incorporated.