• DocumentCode
    2898197
  • Title

    A Neural Networks-Based Clustering Collaborative Filtering Algorithm in E-Commerce Recommendation System

  • Author

    Mai, Jianying ; Fan, Yongjian ; Shen, Yanguang

  • Author_Institution
    Artillery Command Acad. PLA, China
  • fYear
    2009
  • fDate
    7-8 Nov. 2009
  • Firstpage
    616
  • Lastpage
    619
  • Abstract
    E-commerce recommendation system is one of the most important and the most successful application field of data mining technology. Recommendation algorithm is the core of the recommendation system. In this paper, a neural networks-based clustering collaborative filtering algorithm in e-commerce recommendation system is designed, trying to establish an classifier model based on BP neural network for the pre-classification to items and giving realization of clustering collaborative filtering algorithm and BP neural network algorithm, and carrying on the analysis and discussion to this algorithm from multiple aspects. This algorithm is helpful to improve sparsity problem of collaborative filtering algorithm and to form the more effective and the more accurate recommendation result.
  • Keywords
    backpropagation; data mining; electronic commerce; groupware; information filtering; neural nets; pattern classification; pattern clustering; recommender systems; backpropagation neural network; classifier model; clustering collaborative filtering algorithm; data mining technology; e-commerce recommendation system; Algorithm design and analysis; Clustering algorithms; Data mining; Filtering algorithms; Information systems; International collaboration; Nearest neighbor searches; Neural networks; Programmable logic arrays; Real time systems; clustering algorithm; collaborative filtering algorithm; neural networks; recommendation algorithm; recommendation system;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Web Information Systems and Mining, 2009. WISM 2009. International Conference on
  • Conference_Location
    Shanghai
  • Print_ISBN
    978-0-7695-3817-4
  • Type

    conf

  • DOI
    10.1109/WISM.2009.129
  • Filename
    5368343