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We describe and study a model for an Automated Online Recommendation System(AORS) in which a user's preferences can be time-dependent and can also dependon the history of past recommendations and play-outs. The three key features ofthe model that makes it more realistic compared to existing models forrecommendation systems are (1) user preference is inherently latent, (2)current recommendations can affect future preferences, and (3) it allows forthe development of learning algorithms with provable performance guarantees.The problem is cast as an average-cost restless multi-armed bandit for a givenuser, with an independent partially observable Markov decision process (POMDP)for each item of content. We analyze the POMDP for a single arm, describe itsstructural properties, and characterize its optimal policy. We then develop aThompson sampling-based online reinforcement learning algorithm to learn theparameters of the model and optimize utility from the binary responses of theusers to continuous recommendations. We then analyze the performance of thelearning algorithm and characterize the regret. Illustrative numerical resultsand directions for extension to the restless hidden Markov multi-armed banditproblem are also presented.

Optimal Recommendation to Users that React: Online Learning for a Class of POMDPs