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Abstract: Since user's relevance judgments are a source of evidence for information retrieval, learning from this feedback is an appealing idea. Many different learning techniques have successfully been used for relevance feedback. In most models, learning is either performed off line or is based on simple heuristics. The approach we propose is based on a classical probabilistic model in which learning from feedback is simple and incremental. In this paper, we extend this model, presenting a new... (Update)
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BibTeX entry: (Update)
@inproceedings{ piwowarski00:_learn_infor_retriev,
author = {Benjamin Piwowarski},
title = {Learning in Information Retrieval: a Probabilistic Differential Approach},
booktitle = {Proceedings of the BCS-IRSG, 22nd Annual Colloquium on Information Retrieval Research},
year = {2000},
address = {Cambridge, England},
month = {apr},
organization = {Sidney Sussex College},
url = {citeseer.ist.psu.edu/piwowarski00learning.html} }
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