Collaborative Filtering Bandits

TLDR

Classical collaborative filtering and content‑based methods learn static recommendation models, which are inadequate for highly dynamic domains such as news and computational advertising where users and items change rapidly. This study investigates an adaptive clustering technique for content recommendation that leverages exploration–exploitation strategies within contextual multi‑armed bandit frameworks. The algorithm dynamically groups users and items based on collaborative interactions, and includes a regret analysis under a standard linear stochastic noise model. Empirical results on medium‑size real‑world datasets demonstrate that the method scales well and achieves higher click‑through rates than state‑of‑the‑art clustering bandit approaches, effectively exploiting preference patterns similar to collaborative filtering.

Abstract

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.