Kernalized collaborative contextual bandits

We tackle the problem of recommending products in the online recommendation scenario, which occurs many times in real applications. The most famous and explored instances are news recommendations and advertisements. In this work we propose an extension to the state of the art Bandit models to not only take care of different users' interactions, but also to go beyond the linearity assumption of the expected reward. As applicative case we may consider situations in which the number of actions (products) is too big to sample all of them even once, and at the same time we have several changing users to serve content to