Joint features regression for Cold-Start Recommendation on VideoLectures.Net
ECML/PKDD Discovery Challenge Workshop 2011, DCW 2011, Athens, Yunanistan, 05 Eylül 2011, cilt.770, ss.103-109, (Tam Metin Bildiri)
- 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.