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

Abstract

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.