Con-CNAME: A contextual multi-armed bandit algorithm for personalized recommendations

Abstract

Reinforcement learning algorithms play an important role in modern day and have been applied to many domains. For example, personalized recommendations problem can be modelled as a contextual multi-armed bandit problem in reinforcement learning. In this paper, we propose a contextual bandit algorithm which is based on Contexts and the Chosen Number of Arm with Minimal Estimation, namely Con-CNAME in short. The continuous exploration and context used in our algorithm can address the cold start problem in recommender systems. Furthermore, the Con-CNAME algorithm can still make recommendations under the emergency circumstances where contexts are unavailable suddenly. In the experimental evaluation, the reference range of key parameters and the stability of Con-CNAME are discussed in detail. In addition, the performance of Con-CNAME is compared with some classic algorithms. Experimental results show that our algorithm outperforms several bandit algorithms.