• DocumentCode
    133718
  • Title

    Recommender systems in e-commerce

  • Author

    Sivapalan, Sanjeevan ; Sadeghian, Alireza ; Rahnama, Hossein ; Madni, Asad M.

  • Author_Institution
    Comput. Sci. Dept., Ryerson Univ., Toronto, ON, Canada
  • fYear
    2014
  • fDate
    3-7 Aug. 2014
  • Firstpage
    179
  • Lastpage
    184
  • Abstract
    Internet is speeding up and modifying the manner in which daily tasks such as online shopping, paying utility bills, watching new movies, communicating, etc., are accomplished. As an example, in older shopping methods, products were mass produced for a single market and audience but that approach is no longer viable. Markets based on long product and development cycles can no longer survive. To stay competitive, markets need to provide different products and services to different customers with different needs. The shift to online shopping has made it incumbent on producers and retailers to customize for customers´ needs while providing more options than were possible before. This, however, poses a problem for customers who must now analyze every offering in order to determine what they actually need and will benefit from. To aid customers in this scenario, we discuss about common recommender systems techniques that have been employed and their associated trade-offs.
  • Keywords
    collaborative filtering; data mining; electronic commerce; recommender systems; association rules; collaborative filtering; customer need customization; daily tasks; e-commerce; mass produced products; recommender systems; Art; Correlation; Filtering; Size measurement; E-Commerce; Online communications; Online shopping; Recommender Systems;
  • fLanguage
    English
  • Publisher
    ieee
  • Conference_Titel
    World Automation Congress (WAC), 2014
  • Conference_Location
    Waikoloa, HI
  • Type

    conf

  • DOI
    10.1109/WAC.2014.6935763
  • Filename
    6935763