Relevance Feedback for Best Match Term Weighting Algorithms in Information Retrieval
D. Hiemstra, and S. Robertson. DELOS Workshop: Personalisation and Recommender Systems in Digital Libraries, (2001)
Abstract
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...
%0 Conference Paper
%1 citeulike:1651050
%A Hiemstra, Djoerd
%A Robertson, Stephen
%B DELOS Workshop: Personalisation and Recommender Systems in Digital Libraries
%D 2001
%K binary, bm25, feedback, independence, information, ir, language, model, relevance, retrieval, slm
%T Relevance Feedback for Best Match Term Weighting Algorithms in Information Retrieval
%U http://citeseer.ist.psu.edu/hiemstra01relevance.html
%X 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...
@inproceedings{citeulike:1651050,
abstract = {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...},
added-at = {2008-06-17T16:01:02.000+0200},
author = {Hiemstra, Djoerd and Robertson, Stephen},
biburl = {https://www.bibsonomy.org/bibtex/2eabadd9109c196143c010165bc275af4/pprett},
booktitle = {DELOS Workshop: Personalisation and Recommender Systems in Digital Libraries},
citeulike-article-id = {1651050},
interhash = {a4bb71ae71ed003de24f96b400ac945f},
intrahash = {eabadd9109c196143c010165bc275af4},
keywords = {binary, bm25, feedback, independence, information, ir, language, model, relevance, retrieval, slm},
posted-at = {2007-12-03 09:29:30},
priority = {2},
timestamp = {2008-06-17T16:01:40.000+0200},
title = {Relevance Feedback for Best Match Term Weighting Algorithms in Information Retrieval},
url = {http://citeseer.ist.psu.edu/hiemstra01relevance.html},
year = 2001
}