Efficient Thompson sampling for online matrix-factorization recommendation

Jaya Kawale, Hung Bui, Branislav Kveton, Long Tran Thanh, Sanjay Chawla

Research output: Contribution to journalConference articlepeer-review

86 Scopus citations

Abstract

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 relevant items with exploring new or less-recommended items. Our approach, called Particle Thompson sampling for MF (PTS), 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 PTS significantly outperforms the current state-of-the-arts.

Original languageEnglish
Pages (from-to)1297-1305
Number of pages9
JournalAdvances in Neural Information Processing Systems
Volume2015-January
StatePublished - 2015
Externally publishedYes
Event29th Annual Conference on Neural Information Processing Systems, NIPS 2015 - Montreal, Canada
Duration: Dec 7 2015Dec 12 2015

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