Restless bandits that hide their hand and recommendation systems

We consider a restless multi-armed bandit (RMAB) in which each arm can be in one of two states, say 0 or 1. Playing the arm brings it to state 0 with probability one and not playing it induces state transitions with arm-dependent probabilities. Playing an arm generates a unit reward with a probability that depends on the state of the arm. The belief about the state of the arm can be calculated using a Bayesian update after every play. This RMAB has been designed for use in recommendation systems which in turn can be used in applications like creating of playlists or placement of advertisements. In this paper we analyse the RMAB by first showing that it is Whittle-indexable and then obtain a closed form expression for the Whittle index for each arm calculated from the belief about its state and the parameters that describe the arm. For an RMAB to be useful in practice, we need to be able to learn the parameters of the arms. We present an algorithm derived from Thompson sampling scheme, that learns the parameters of the arms and also evaluate its performance numerically.