A simulated study of implicit feedback models

R.W. White, J.M. Jose, C.J. van Rijsbergen, I. Ruthven, S. MacDonald (Editor), J. Tait (Editor)

Research output: Chapter in Book/Report/Conference proceedingChapter

27 Citations (Scopus)
48 Downloads (Pure)


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.
Original languageEnglish
Title of host publicationAdvances in Information Retrieval
Subtitle of host publicationProceedings of the 26th European Conference in Information Retrieval (ECIR 2004)
Place of PublicationBerlin-Heidelberg
Number of pages15
ISBN (Print)3540213821
Publication statusPublished - 2004
Event26th European Conference on Information Retrieval (ECIR) - Sunderland, UK
Duration: 5 Apr 20047 Apr 2004

Publication series

NameLecture Notes in Computer Science


Conference26th European Conference on Information Retrieval (ECIR)
CitySunderland, UK


  • implicit feedback
  • computer science
  • information retrieval
  • web search
  • relevance ranking


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