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Efficient Thompson sampling for online matrix-factorization recommendation

Efficient Thompson sampling for online matrix-factorization recommendation
Efficient Thompson sampling for online matrix-factorization recommendation
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.
1297-1305
Kawale, Jaya
679ace39-ef5d-4ed2-9be6-adca97cd402c
Bui, Hung
eeba59db-466b-4278-bf57-7ac13883be5f
Kveton, Branislav
416796f9-3f66-4d2c-98a6-3575e9102775
Tran-Thanh, Long
e0666669-d34b-460e-950d-e8b139fab16c
Chawla, Sanjay
bc56b9e4-fa0c-4a03-8565-4506bc5a42e4
Kawale, Jaya
679ace39-ef5d-4ed2-9be6-adca97cd402c
Bui, Hung
eeba59db-466b-4278-bf57-7ac13883be5f
Kveton, Branislav
416796f9-3f66-4d2c-98a6-3575e9102775
Tran-Thanh, Long
e0666669-d34b-460e-950d-e8b139fab16c
Chawla, Sanjay
bc56b9e4-fa0c-4a03-8565-4506bc5a42e4

Kawale, Jaya, Bui, Hung, Kveton, Branislav, Tran-Thanh, Long and Chawla, Sanjay (2015) Efficient Thompson sampling for online matrix-factorization recommendation. Neural Information Processing Systems (NIPS 2015), Canada. 07 - 12 Dec 2015. pp. 1297-1305 .

Record type: Conference or Workshop Item (Paper)

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 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.

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More information

Accepted/In Press date: 10 September 2015
Published date: 10 December 2015
Venue - Dates: Neural Information Processing Systems (NIPS 2015), Canada, 2015-12-07 - 2015-12-12
Organisations: Agents, Interactions & Complexity

Identifiers

Local EPrints ID: 387963
URI: https://eprints.soton.ac.uk/id/eprint/387963
PURE UUID: 9955c551-38e9-4ad1-83ae-7c81ee6d09cb
ORCID for Long Tran-Thanh: ORCID iD orcid.org/0000-0003-1617-8316

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Date deposited: 17 Feb 2016 13:45
Last modified: 06 Jun 2018 12:35

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