A Simulated Study of Implicit Feedback Models

In this paper we report on a study of implicit feedback models for unobtrusively tracking the information needs of searchers. Such models use relevance information gathered from searcher interaction and can be a potential substitute for explicit relevance feedback. We introduce a variety of implicit feedback models designed to enhance an Information Retrieval (IR) system's representation of searchers' information needs. To benchmark their performance we use a simulation-centric evaluation methodology that measures how well each model learns relevance and improves search effectiveness. The results show that a heuristic-based binary voting model and one based on Jeffrey's rule of conditioning [5] outperform the other models under investigation.

• Type: Conference
• Year: 2004
• Where: ECIR
• Authors: Ryen W. White, Joemon M. Jose, C. J. van Rijsbergen, Ian Ruthven
• Keywords: ECIR 2004, Explicit Relevance Feedback, Implicit Feedback Models, Information Needs, Information Technology