Jean-Michel Renders
ECIR, Padua, Italy; March 20-23, 2016.
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.
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