Latent Contextual Bandits and their Application to Personalized Recommendations for New Users
This addresses the cold-start problem in recommendation systems, offering a more efficient method for learning new users' interests, though it appears incremental as it builds on existing contextual bandit frameworks.
The paper tackles the cold-start problem in personalized recommendations for new users by proposing Latent Contextual Bandits, which leverages learned latent user classes to improve efficiency, achieving a better regret bound and demonstrating benefits on a real-world dataset and user study.
Personalized recommendations for new users, also known as the cold-start problem, can be formulated as a contextual bandit problem. Existing contextual bandit algorithms generally rely on features alone to capture user variability. Such methods are inefficient in learning new users' interests. In this paper we propose Latent Contextual Bandits. We consider both the benefit of leveraging a set of learned latent user classes for new users, and how we can learn such latent classes from prior users. We show that our approach achieves a better regret bound than existing algorithms. We also demonstrate the benefit of our approach using a large real world dataset and a preliminary user study.