Abstract
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 language | English |
|---|---|
| Title of host publication | Advances in Information Retrieval |
| Subtitle of host publication | Proceedings of the 26th European Conference in Information Retrieval (ECIR 2004) |
| Place of Publication | Berlin-Heidelberg |
| Publisher | Springer-Verlag |
| Pages | 311-326 |
| Number of pages | 15 |
| Volume | 2997 |
| ISBN (Print) | 3540213821 |
| Publication status | Published - 2004 |
| Event | 26th European Conference on Information Retrieval (ECIR) - Sunderland, UK Duration: 5 Apr 2004 → 7 Apr 2004 |
Publication series
| Name | Lecture Notes in Computer Science |
|---|---|
| Publisher | Springer-Verlag |
Conference
| Conference | 26th European Conference on Information Retrieval (ECIR) |
|---|---|
| City | Sunderland, UK |
| Period | 5/04/04 → 7/04/04 |
Keywords
- implicit feedback
- computer science
- information retrieval
- web search
- relevance ranking
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Cite this
White, R. W., Jose, J. M., van Rijsbergen, C. J., Ruthven, I., MacDonald, S. (Ed.), & Tait, J. (Ed.) (2004). A simulated study of implicit feedback models. In Advances in Information Retrieval: Proceedings of the 26th European Conference in Information Retrieval (ECIR 2004) (Vol. 2997, pp. 311-326). (Lecture Notes in Computer Science). Springer-Verlag.