An Empirical Evaluation of Context Aware Clustering of Bandits using Thompson Sampling

Abstract: Stochastic bandit algorithms are increasingly being used in the domain of recommender systems, when the environment is very dynamic and the items to recommend are frequently changing over time. While traditional approaches consider a single bandit instance which assumes all users to be equal, recent developments in the literature showed that the quality of recommendations can be improved when individual bandit instances for different users are considered and clustering techniques are used. In this work we develop an algorithm which clusters users based on the context at disposal using a Bayesian framework called Thompson Sampling, opposed to a similar algorithm called CAB recently presented at ICML 2017 (International Conference on Machine Learning), which uses a deterministic strategy. We show extensively through experiments on synthetic and real world data that the perfor- mance of our algorithm is in line with its deterministic version CAB when it comes to the quality of recommendations. On the other hand, our approach is relatively faster when considering running time.