Optimal Recommendation to Users that React: Online Learning for a Class of POMDPs
This work addresses the challenge of dynamic user preferences in automated recommendation systems, offering a more realistic model with theoretical guarantees, though it appears incremental as it builds on existing bandit and POMDP frameworks.
The authors tackled the problem of online recommendation systems with time-dependent user preferences influenced by past interactions, by modeling it as a restless multi-armed bandit with POMDPs and developing a Thompson sampling-based algorithm. They provided provable performance guarantees and characterized regret, with illustrative numerical results included.
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 depend on the history of past recommendations and play-outs. The three key features of the model that makes it more realistic compared to existing models for recommendation systems are (1) user preference is inherently latent, (2) current recommendations can affect future preferences, and (3) it allows for the development of learning algorithms with provable performance guarantees. The problem is cast as an average-cost restless multi-armed bandit for a given user, with an independent partially observable Markov decision process (POMDP) for each item of content. We analyze the POMDP for a single arm, describe its structural properties, and characterize its optimal policy. We then develop a Thompson sampling-based online reinforcement learning algorithm to learn the parameters of the model and optimize utility from the binary responses of the users to continuous recommendations. We then analyze the performance of the learning algorithm and characterize the regret. Illustrative numerical results and directions for extension to the restless hidden Markov multi-armed bandit problem are also presented.