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


ECML/PKDD Discovery Challenge Workshop 2011, DCW 2011, Athens, Greece, 05 September 2011, vol.770, pp.103-109 identifier

  • Publication Type: Conference Paper / Full Text
  • Volume: 770
  • City: Athens
  • Country: Greece
  • Page Numbers: pp.103-109
  • Anadolu University Affiliated: Yes


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 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.