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DELOS
2001

Relevance Feedback for Best Match Term Weighting Algorithms in Information Retrieval

9 years 12 months ago
Relevance Feedback for Best Match Term Weighting Algorithms in Information Retrieval
Personalisation in full text retrieval or full text filtering implies reweighting of the query terms based on some explicit or implicit feedback from the user. Relevance feedback inputs the user's judgements on previously retrieved documents to construct a personalised query or user profile. This paper studies relevance feedback within two probabilistic models of information retrieval: the first based on statistical language models and the second based on the binary independence probabilistic model. The paper shows the resemblance of the approaches to relevance feedback of these models, introduces new approaches to relevance feedback for both models, and evaluates the new relevance feedback algorithms on the TREC collection. The paper shows that there are no significant differences between simple and sophisticated approaches to relevance feedback.
Djoerd Hiemstra, Stephen E. Robertson
Added 31 Oct 2010
Updated 31 Oct 2010
Type Conference
Year 2001
Where DELOS
Authors Djoerd Hiemstra, Stephen E. Robertson
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