• DocumentCode
    2990221
  • Title

    Research on personalized recommendation model for mobile advertising

  • Author

    Gu Qi-wei ; Guo Peng

  • Author_Institution
    Coll. of Manage., Shenzhen Univ., Shenzhen, China
  • fYear
    2012
  • fDate
    20-22 Sept. 2012
  • Firstpage
    59
  • Lastpage
    63
  • Abstract
    Aimed at enhancing the accuracy of the personalized recommendation for mobile advertising, overcoming the shortcomings of the traditional similarity calculation and collaborative filtering recommendation techniques, the cloud model calculation method improved the strict item or project matching problem in traditional similarity calculation, resolved extreme sparse data problem. And a mixed recommendation model is established based on mobile advertisement, content recommendation and linear combination of collaborative filtering recommendation. Experiments prove that the new method has obviously smaller MAE and higher quality in recommendation system.
  • Keywords
    advertising; cloud computing; collaborative filtering; mobile computing; recommender systems; cloud model calculation method; collaborative filtering recommendation techniques; content recommendation; extreme sparse data problem; linear combination; mixed recommendation model; mobile advertisement; mobile advertising; personalized recommendation model; project matching problem; similarity calculation; strict item; Advertising; Collaboration; Computational modeling; Educational institutions; Filtering; Mobile communication; Predictive models; E-commerce; cloud model; mixed recommendation; mobile advertising; recommendation system;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    Management Science and Engineering (ICMSE), 2012 International Conference on
  • Conference_Location
    Dallas, TX
  • ISSN
    2155-1847
  • Print_ISBN
    978-1-4673-3015-2
  • Type

    conf

  • DOI
    10.1109/ICMSE.2012.6414161
  • Filename
    6414161