Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/16356
Title: A split-list approach for relevance feedback in information retrieval
Authors: Wu, HC
Luk, RWP 
Wong, KF
Nie, JY
Keywords: Active feedback
Document-context
Experimentation
Issue Date: 2012
Publisher: Pergamon Press
Source: Information processing and management, 2012, v. 48, no. 5, p. 969-977 How to cite?
Journal: Information processing and management 
Abstract: In this paper we present a new algorithm for relevance feedback (RF) in information retrieval. Unlike conventional RF algorithms which use the top ranked documents for feedback, our proposed algorithm is a kind of active feedback algorithm which actively chooses documents for the user to judge. The objectives are (a) to increase the number of judged relevant documents and (b) to increase the diversity of judged documents during the RF process. The algorithm uses document-contexts by splitting the retrieval list into sub-lists according to the query term patterns that exist in the top ranked documents. Query term patterns include a single query term, a pair of query terms that occur in a phrase and query terms that occur in proximity. The algorithm is an iterative algorithm which takes one document for feedback in each of the iterations. We experiment with the algorithm using the TREC-6, -7, -8, -2005 and GOV2 data collections and we simulate user feedback using the TREC relevance judgements. From the experimental results, we show that our proposed split-list algorithm is better than the conventional RF algorithm and that our algorithm is more reliable than a similar algorithm using maximal marginal relevance.
URI: http://hdl.handle.net/10397/16356
ISSN: 0306-4573
DOI: 10.1016/j.ipm.2012.03.007
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