• DocumentCode
    3695989
  • Title

    Comparing Preference Models in Recommender Systems

  • Author

    Juntao Liu;Dewei Deng;Hanbao Wu;Caihua Wu

  • Author_Institution
    709th Res. Inst., China Shipbuilding Ind. Corp., Wuhan, China
  • Volume
    1
  • fYear
    2015
  • Firstpage
    210
  • Lastpage
    213
  • Abstract
    How to represent the users´ preference is one of the principle problems in widely used recommender systems. To address this problem, in this paper, the expressibility, space complexity and learning complexity of different kinds of preference models are investigated. The recommendation performances of these models are also compared on a real-life dataset. Considering both the expressibility and computational complexity, the quadric model is the most suited to recommender systems.
  • Keywords
    "Computational modeling","Complexity theory","Regression tree analysis","Recommender systems","Numerical models","Probabilistic logic","Testing"
  • Publisher
    ieee
  • Conference_Titel
    Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2015 7th International Conference on
  • Print_ISBN
    978-1-4799-8645-3
  • Type

    conf

  • DOI
    10.1109/IHMSC.2015.61
  • Filename
    7334688
    • Link To Document
    https://search.isc.ac/dl/search/defaultta.aspx?DTC=49&DC=3695989