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

Jean-Michel Renders
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 sit- uation or during a change - possibly abrupt - in the user needs and item popularity. In this paper, we propose a new adaptive collabora- tive 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 exploita- tion/exploration trade-off} in a single consistent framework, while keep- ing the underlying algorithms efficient and easily scalable. Several exper- iments on real-world datasets show that these adaptation mechanisms significantly improve the quality of recommendations compared to other standard on-line adaptive algorithms and other "fast" learning curves in identifying the user/item profiles, even when they evolve over time.
ECIR, Padua, Italy; March 20-23, 2016.