  Classical collaborative filtering, and content-based filtering methods try to
learn a static recommendation model given training data. These approaches are
far from ideal in highly dynamic recommendation domains such as news
recommendation and computational advertisement, where the set of items and
users is very fluid. In this work, we investigate an adaptive clustering
technique for content recommendation based on exploration-exploitation
strategies in contextual multi-armed bandit settings. Our algorithm takes into
account the collaborative effects that arise due to the interaction of the
users with the items, by dynamically grouping users based on the items under
consideration and, at the same time, grouping items based on the similarity of
the clusterings induced over the users. The resulting algorithm thus takes
advantage of preference patterns in the data in a way akin to collaborative
filtering methods. We provide an empirical analysis on medium-size real-world
datasets, showing scalability and increased prediction performance (as measured
by click-through rate) over state-of-the-art methods for clustering bandits. We
also provide a regret analysis within a standard linear stochastic noise
setting.
