• 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