DocumentCode :
2112693
Title :
Semi-metric Networks for Recommender Systems
Author :
Simas, T. ; Rocha, L.M.
Author_Institution :
Cognitive Sci. Program, Indiana Univ., Bloomington, IN, USA
Volume :
3
fYear :
2012
fDate :
4-7 Dec. 2012
Firstpage :
175
Lastpage :
179
Abstract :
Weighted graphs obtained from co-occurrence in user-item relations lead to non-metric topologies. We use this semi-metric behavior to issue recommendations, and discuss its relationship to transitive closure on fuzzy graphs. Finally, we test the performance of this method against other item- and user-based recommender systems on the Movie lens benchmark. We show that including highly semi-metric edges in our recommendation algorithms leads to better recommendations.
Keywords :
entertainment; fuzzy set theory; graph theory; network theory (graphs); recommender systems; Movielens benchmark; fuzzy graphs; item-based recommender systems; nonmetric topologies; recommendation algorithms; semimetric network edges; transitive closure; user-based recommender systems; user-item relations; weighted graphs; complex networks; fuzzy systems; network theory (graphs); recommender systems;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Web Intelligence and Intelligent Agent Technology (WI-IAT), 2012 IEEE/WIC/ACM International Conferences on
Conference_Location :
Macau
Print_ISBN :
978-1-4673-6057-9
Type :
conf
DOI :
10.1109/WI-IAT.2012.245
Filename :
6511672
Link To Document :
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