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Efficient Thompson Sampling for Online ´┐╝Matrix-Factorization Recommendation
Jaya Kawale · Hung H Bui · Branislav Kveton · Long Tran-Thanh · Sanjay Chawla

Thu Dec 10 08:00 AM -- 12:00 PM (PST) @ 210 C #43

Matrix factorization (MF) collaborative filtering is an effective and widely used method in recommendation systems. However, the problem of finding an optimal trade-off between exploration and exploitation (otherwise known as the bandit problem), a crucial problem in collaborative filtering from cold-start, has not been previously addressed.In this paper, we present a novel algorithm for online MF recommendation that automatically combines finding the most relevantitems with exploring new or less-recommended items.Our approach, called Particle Thompson Sampling for Matrix-Factorization, is based on the general Thompson sampling framework, but augmented with a novel efficient online Bayesian probabilistic matrix factorization method based on the Rao-Blackwellized particle filter.Extensive experiments in collaborative filtering using several real-world datasets demonstrate that our proposed algorithm significantly outperforms the current state-of-the-arts.

Author Information

Jaya Kawale (Adobe Research)
Hung H Bui (Adobe Research)
Branislav Kveton (Adobe Research)
Long Tran-Thanh (University of Southampton)
Sanjay Chawla (Qatar Computing Research Institute, HBKU and University of Sydney)

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