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