Joint features regression for Cold-Start Recommendation on VideoLectures.Net

ÇAPAN G., YILMAZEL Ö.

ECML/PKDD Discovery Challenge Workshop 2011, DCW 2011, Athens, Yunanistan, 05 Eylül 2011, cilt.770, ss.103-109 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Cilt numarası: 770
  • Basıldığı Şehir: Athens
  • Basıldığı Ülke: Yunanistan
  • Sayfa Sayıları: ss.103-109
  • Anadolu Üniversitesi Adresli: Evet

Özet

Recommender systems are popular information filtering systems used in various domains. Cold-start problem is a key challenge in a recommender system. In newitem/existing-user case of the cold-start problem, which is recommendation of a recently-arrived item to a user with historical data, finding links between existing items with recently-arrived items is critical. Using VideoLectures.net Cold-Start Recommendation Challenge data, this paper includes a linear regression model to predict future co-viewing count between an existing item and a recently-arrived, not-yet-viewed item.