published by Tishreen University
in 2014
in
and research's language is
العربية
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Abstract in English
Recommender systems represents a class of systems designed to help individuals deal with information overload or incomplete
information. Such systems help individuals by providing recommendation through the use of various personalization
techniques. Collaborative filtering is a widely used technique for rating prediction in recommender systems. This paper presents a
method uses preference relations instead of absolute ratings for similarity calculation.
The result indicates that the proposed method outperform the other methods such as the Somers Coefficient.
References used
Desarkar M. et all., 2010 –Aggregating Preference Graphs for Collaborative Rating, RecSys, Proc. 4th ACM conference on Recommender systems.New York, USA
Chen Y.L. et all., 2008 – A novel collaborative filtering approach for recommending ranked items, Expert System with application
Jin R. et all., 2003 – Preference-based graphics models for collaborative filtering, UAI, page 329-336
Yildirim H., Kishnamoorthy M. S., 2008 – A random Walk method for alleviating the sparsity in collaborative filtering. RecSys, ACM, new York, USA, page 131 – 138
Moshfeghi D. et all., 2009 – Movie recommender: Sementically emrichedunified relevance model for rating predication in collaborative filtering, in ECIR page 54 – 65