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, Hırvatistan, 28 Eylül - 02 Ekim 2020, ss.186-191 identifier identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.23919/mipro48935.2020.9245195
  • Basıldığı Şehir: Opatija
  • Basıldığı Ülke: Hırvatistan
  • Sayfa Sayıları: ss.186-191
  • Anahtar Kelimeler: Recommendation Systems, Collaborative Filtering, Memory Based Classification, Recommendation Performance
  • Orta Doğu Teknik Üniversitesi Adresli: Hayır

Özet

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.