A Scalable K-Nearest Neighbor Algorithm for Recommendation System Problems

Sagdic A., Tekinbas C., Arslan E., Kucukyilmaz T.

43rd International Convention on Information, Communication and Electronic Technology (MIPRO), Opatija, Croatia, 28 September - 02 October 2020, pp.186-191 identifier identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.23919/mipro48935.2020.9245195
  • City: Opatija
  • Country: Croatia
  • Page Numbers: pp.186-191
  • Keywords: Recommendation Systems, Collaborative Filtering, Memory Based Classification, Recommendation Performance
  • TED University Affiliated: Yes

Abstract

Memory-based classification techniques are commonly used for modeling recommendation problems. They rely on the intuition that similar users and/or items behave similarly, facilitating user-toitem, item-to-item, or user-to-user proximities. A significant drawback of memory-based classification techniques is that they perform poorly with large scale data. Thus, using the off-the-shelf classification techniques for recommendation problems generally lead to impractical computational costs.