research-article

Retargeted matrix factorization for collaborative filtering

Authors:

Oluwasanmi Koyejo,

Sreangsu Acharyya,

Joydeep GhoshAuthors Info & Claims

RecSys '13: Proceedings of the 7th ACM conference on Recommender systems

Pages 49 - 56

https://doi.org/10.1145/2507157.2507185

Published: 12 October 2013 Publication History

Abstract

This paper introduces retargeted matrix factorization (R-MF); a novel approach for learning the user-wise ranking of items in the context of collaborative filtering. R-MF learns to rank by "retargeting" the item ratings of each user, searching for a monotonic transformation of the ratings that results in a better fit while preserving the ranked order of each user's ratings. The retargeting is combined with an underlying matrix factorization regression model that couples the user-wise rankings to exploit shared low dimensional structure. We show that R-MF recovers a unique solution under mild conditions, and propose a simple and efficient optimization scheme that alternates between retargeting the ratings subject to ordering constraints, and matrix factorization regression. The retargeting step is independent for each user, and is trivially parallelized. The ranking performance of retargeted matrix factorization is evaluated on benchmark movie recommendation datasets and results in superior ranking performance compared to collaborative filtering algorithms specifically designed to optimize ranking metrics.

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Li LLiu PBu CZhang ZWu X(2024)Fuzzy Ranking-Based Preference Completion via Graph Pattern Matching and RematchingIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2024.33590968:2(2009-2021)Online publication date: Apr-2024
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Index Terms

Retargeted matrix factorization for collaborative filtering

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cover image ACM Conferences

RecSys '13: Proceedings of the 7th ACM conference on Recommender systems

October 2013

516 pages

ISBN:9781450324090

DOI:10.1145/2507157

General Chairs:
Qiang Yang
Huawei Noah Ark Research Lab, China
,
Irwin King
Chinese University of Hong Kong, China
,
Qing Li
City University of Hong Kong, China
,
Program Chairs:
Pearl Pu
Ecole Polytechnique Fédérale de Lausanne, Switzerland
,
George Karypis
University of Minnesota, USA

Copyright © 2013 ACM.

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Published: 12 October 2013

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RecSys '13

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RecSys '13: Seventh ACM Conference on Recommender Systems

October 12 - 16, 2013

Hong Kong, China

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RecSys '13 Paper Acceptance Rate 32 of 136 submissions, 24%;

Overall Acceptance Rate 254 of 1,295 submissions, 20%

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https://dl.acm.org/doi/10.1145/3686397.3686411
Li LLiu PBu CZhang ZWu X(2024)Fuzzy Ranking-Based Preference Completion via Graph Pattern Matching and RematchingIEEE Transactions on Emerging Topics in Computational Intelligence10.1109/TETCI.2024.33590968:2(2009-2021)Online publication date: Apr-2024
https://doi.org/10.1109/TETCI.2024.3359096
Zhang YLi WLi HTao RDu Q(2020)Discriminative Marginalized Least-Squares Regression for Hyperspectral Image ClassificationIEEE Transactions on Geoscience and Remote Sensing10.1109/TGRS.2019.294908258:5(3148-3161)Online publication date: May-2020
https://doi.org/10.1109/TGRS.2019.2949082
Wen JZhong ZZhang ZFei LLai ZChen R(2020)Adaptive Locality Preserving RegressionIEEE Transactions on Circuits and Systems for Video Technology10.1109/TCSVT.2018.288972730:1(75-88)Online publication date: Jan-2020
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Lu JLin JLai ZWang HZhou J(2020)Target Redirected Regression with Dynamic Neighborhood StructureInformation Sciences10.1016/j.ins.2020.08.062Online publication date: Sep-2020
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Wang ZXu QMa KJiang YCao XHuang QAmsaleg LHuet BLarson MGravier GHung HNgo CTsang Ooi W(2019)Adversarial Preference Learning with Pairwise ComparisonsProceedings of the 27th ACM International Conference on Multimedia10.1145/3343031.3350919(656-664)Online publication date: 15-Oct-2019
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