Exploiting session context for information retrieval: a comparative study

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.