Abstract
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.
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Notes
- 1.
Vodkaster (http://www.vodkaster.com) is a French movie recommendation website, dedicated to rather movie-educated people.
- 2.
It is easy to show that we can divide all values of the hyper-parameters by \(\sigma ^2\) without changing the predicted value; so we can fix \(\sigma ^2\) to 1.
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Acknowledgement
This work was partially funded by the French Government under the grant \(<\)ANR-13-CORD-0020\(>\) (ALICIA Project).
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Renders, JM. (2016). Adaptive Collaborative Filtering with Extended Kalman Filters and Multi-armed Bandits. In: Ferro, N., et al. Advances in Information Retrieval. ECIR 2016. Lecture Notes in Computer Science(), vol 9626. Springer, Cham. https://doi.org/10.1007/978-3-319-30671-1_46
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DOI: https://doi.org/10.1007/978-3-319-30671-1_46
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