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.
CITATION STYLE
Renders, J. M. (2016). Adaptive collaborative filtering with extended kalman filters and multi-armed bandits. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 9626, pp. 626–638). Springer Verlag. https://doi.org/10.1007/978-3-319-30671-1_46
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