Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/39812
Title: Probabilistic document-context based relevance feedback with limited relevance judgments
Authors: Wu, HC
Luk, RWP 
Wong, KF
Kwok, KL
Keywords: Document-context
Model
Issue Date: 2006
Source: CIKM '06 Proceedings of the 15th ACM International Conference on Information and Knowledge Management, Washington, D.C., USA., 6-11 Nov 2006, p. 854-855 How to cite?
Abstract: This paper presents our novel relevance feedback (RF) algorithm that uses the probabilistic document-context based retrieval model with limited relevance judgments for document re-ranking. Probabilities of the document-context based retrieval model are estimated from the top N (=20) documents in the initial retrieval. We use document-context based cosine similarity measure to find similar data for better probability estimation in order to reduce the data scarcity problem and the negative weighting problem. Our RF algorithm is promising because its mean average precision is statistically significantly better than the baseline using TREC-6 and TREC-7 data collections.
URI: http://hdl.handle.net/10397/39812
ISBN: 1-59593-433-2
DOI: 10.1145/1183614.1183764
Appears in Collections:Conference Paper

Access
View full-text via PolyU eLinks SFX Query
Show full item record

SCOPUSTM   
Citations

3
Last Week
1
Last month
Citations as of Sep 17, 2017

Page view(s)

34
Last Week
4
Last month
Checked on Sep 24, 2017

Google ScholarTM

Check

Altmetric



Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.