Adaptive Collaborative Filtering with Extended Kalman Filters and Multi-armed Bandits

It is now widely recognized that, as real-world recommender systems are often facing drifts in users’ preferences and shifts in items’ perception, collaborative filtering methods have to cope with these time-varying effects. Furthermore, they have to constantly control the trade-off between exploration and exploitation, whether in a cold start situation or during a change - possibly abrupt - in the user needs and item popularity. In this paper, we propose a new adaptive collaborative filtering method, coupling Matrix Completion, extended non-linear Kalman filters and Multi-Armed Bandits. The main goal of this method is exactly to tackle simultaneously both issues – adaptivity and exploitation/exploration trade-off – in a single consistent framework, while keeping the underlying algorithms efficient and easily scalable. Several experiments on real-world datasets show that these adaptation mechanisms significantly improve the quality of recommendations compared to other standard on-line adaptive algorithms and offer “fast” learning curves in identifying the user/item profiles, even when they evolve over time.