Cold-start Problems in Recommendation Systems via Contextual-bandit Algorithms
This addresses the cold-start issue in recommendation systems for new users, which is an incremental improvement over existing methods.
The paper tackles the cold-start problem in recommendation systems for new users by framing it as a contextual-bandit problem, using past user ratings as context without needing additional information, and shows that the proposed method significantly outperforms state-of-the-art techniques on datasets like Movielens, Netflix, and Yahoo!Music.
In this paper, we study a cold-start problem in recommendation systems where we have completely new users entered the systems. There is not any interaction or feedback of the new users with the systems previoustly, thus no ratings are available. Trivial approaches are to select ramdom items or the most popular ones to recommend to the new users. However, these methods perform poorly in many case. In this research, we provide a new look of this cold-start problem in recommendation systems. In fact, we cast this cold-start problem as a contextual-bandit problem. No additional information on new users and new items is needed. We consider all the past ratings of previous users as contextual information to be integrated into the recommendation framework. To solve this type of the cold-start problems, we propose a new efficient method which is based on the LinUCB algorithm for contextual-bandit problems. The experiments were conducted on three different publicly-available data sets, namely Movielens, Netflix and Yahoo!Music. The new proposed methods were also compared with other state-of-the-art techniques. Experiments showed that our new method significantly improves upon all these methods.