Large-Scale Bandit Approaches for Recommender Systems

Recommender systems have been successfully applied to many application areas to predict users' preference. However, these systems face the exploration-exploitation dilemma when making a recommendation, since they need to exploit items which raise users' interest and explore new items to improve satisfaction simultaneously. In this paper, we deal with this dilemma through Multi-Armed Bandit (MAB) approaches, especially for large-scale recommender systems that have vast or infinite items. We propose two large-scale bandit approaches under the situations that there is no available priori information. The continuous exploration in our approaches can address the cold start problem in recommender systems. Furthermore, our context-free approaches are based on users' click behavior without the dependence on priori information. We theoretically prove that our approaches can converge to optimal item recommendations in the long run. Experimental results indicate that our approaches are able to provide more accurate recommendations than some classic bandit approaches in terms of click-through rates, with less calculation time.