Implicit Feedback for Inferring User Preference: A Bibliography Diane Kelly SCILS, Rutgers University New Brunswick, NJ 08901 USA diane@scils.rutgers .edu Jaime Teevan CSAIL, Massachusetts Institute of Technology Cambridge, MA 02138 USA teevan@csail .mit .edu 1 Introduction Relevance feedback has a history in information retrieval that dates back well over thirty years (c.f [SL96]). Relevance feedback is typically used for query expansion during short-term modeling of a user's immediate information need and for user profiling during long-term modeling of a user's persistent interests and preferences. Traditional relevance feedback methods require that users explicitly give feedback by, for example, specifying keywords, selecting and marking documents, or answering questions about their interests. Such relevance feedback methods force users to engage in additional activities beyond their normal searching behavior . Since the cost to the user is high and the benefits are not always apparent, it can be difficult to collect the necessary data and the effectiveness of explicit techniques can be limited. In this paper we consider the use of implicit feedback techniques for query expansion and user profiling in information retrieval tasks. These techniques unobtrusively obtain information about users by watching their natural interactions with the system. Some of the
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Implicit feedback for inferring user preference: a bibliography
Kelly, Diane; Teevan, Jaime
Follow ACM SIGIR Forum
, Volume 37 (2)
Association for Computing Machinery – Sep 1, 2003
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