Article

Collaborative prediction using ensembles of Maximum Margin Matrix Factorizations

Author:

Dennis DeCosteAuthors Info & Claims

ICML '06: Proceedings of the 23rd international conference on Machine learning

Pages 249 - 256

https://doi.org/10.1145/1143844.1143876

Published: 25 June 2006 Publication History

Abstract

Fast gradient-based methods for Maximum Margin Matrix Factorization (MMMF) were recently shown to have great promise (Rennie & Srebro, 2005), including significantly outperforming the previous state-of-the-art methods on some standard collaborative prediction benchmarks (including MovieLens). In this paper, we investigate ways to further improve the performance of MMMF, by casting it within an ensemble approach. We explore and evaluate a variety of alternative ways to define such ensembles. We show that our resulting ensembles can perform significantly better than a single MMMF model, along multiple evaluation metrics. In fact, we find that ensembles of partially trained MMMF models can sometimes even give better predictions in total training time comparable to a single MMMF model.

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Cited By

Aljunid MD.H. MHooshmand MAli WShetty AAlzoubah S(2025)A collaborative filtering recommender systems: SurveyNeurocomputing10.1016/j.neucom.2024.128718617(128718)Online publication date: Feb-2025
https://doi.org/10.1016/j.neucom.2024.128718
Shyam AKumar VKagita VPujari A(2024)UniRecSys: A unified framework for personalized, group, package, and package-to-group recommendationsKnowledge-Based Systems10.1016/j.knosys.2024.111552289(111552)Online publication date: Apr-2024
https://doi.org/10.1016/j.knosys.2024.111552
Ravakhah MJalali MForghani YSheibani R(2021)Balanced hierarchical max margin matrix factorization for recommendation systemExpert Systems10.1111/exsy.1291139:4Online publication date: 14-Dec-2021
https://doi.org/10.1111/exsy.12911
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Index Terms

Collaborative prediction using ensembles of Maximum Margin Matrix Factorizations
1. Mathematics of computing
  1. Mathematical analysis
    1. Numerical analysis
      1. Computations on matrices

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Published In

cover image ACM Other conferences

ICML '06: Proceedings of the 23rd international conference on Machine learning

June 2006

1154 pages

ISBN:1595933832

DOI:10.1145/1143844

Program Chairs:
William Cohen,
Andrew Moore

Copyright © 2006 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 June 2006

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ICML '06 Paper Acceptance Rate 140 of 548 submissions, 26%;

Overall Acceptance Rate 140 of 548 submissions, 26%

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Cited By

Aljunid MD.H. MHooshmand MAli WShetty AAlzoubah S(2025)A collaborative filtering recommender systems: SurveyNeurocomputing10.1016/j.neucom.2024.128718617(128718)Online publication date: Feb-2025
https://doi.org/10.1016/j.neucom.2024.128718
Shyam AKumar VKagita VPujari A(2024)UniRecSys: A unified framework for personalized, group, package, and package-to-group recommendationsKnowledge-Based Systems10.1016/j.knosys.2024.111552289(111552)Online publication date: Apr-2024
https://doi.org/10.1016/j.knosys.2024.111552
Ravakhah MJalali MForghani YSheibani R(2021)Balanced hierarchical max margin matrix factorization for recommendation systemExpert Systems10.1111/exsy.1291139:4Online publication date: 14-Dec-2021
https://doi.org/10.1111/exsy.12911
Li JWu LGuo RLiu CLiu H(2019)Multi-level network embedding with boosted low-rank matrix approximationProceedings of the 2019 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining10.1145/3341161.3342864(49-56)Online publication date: 27-Aug-2019
https://dl.acm.org/doi/10.1145/3341161.3342864
Xu ZLiu BZhe SBai HWang ZNeville J(2019)Variational Random Function Model for Network ModelingIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2018.283766730:1(318-324)Online publication date: Jan-2019
https://doi.org/10.1109/TNNLS.2018.2837667
Fu YLi DBarlet-Ros PHuang CHuang ZShen SSu H(2019)A Skewness-Aware Matrix Factorization Approach for Mesh-Structured Cloud ServicesIEEE/ACM Transactions on Networking10.1109/TNET.2019.292381527:4(1598-1611)Online publication date: 1-Aug-2019
https://dl.acm.org/doi/10.1109/TNET.2019.2923815
Zhu ZLi QTang GWakin M(2018)Global Optimality in Low-Rank Matrix OptimizationIEEE Transactions on Signal Processing10.1109/TSP.2018.283540366:13(3614-3628)Online publication date: 1-Jul-2018
https://dl.acm.org/doi/10.1109/TSP.2018.2835403
Jalili MAhmadian SIzadi MMoradi PSalehi M(2018)Evaluating Collaborative Filtering Recommender Algorithms: A SurveyIEEE Access10.1109/ACCESS.2018.28837426(74003-74024)Online publication date: 2018
https://doi.org/10.1109/ACCESS.2018.2883742
Li QZhu ZTang G(2018)The non-convex geometry of low-rank matrix optimizationInformation and Inference: A Journal of the IMA10.1093/imaiai/iay0038:1(51-96)Online publication date: 22-Mar-2018
https://doi.org/10.1093/imaiai/iay003
Suh SShin SLee JReddy CChoo J(2018)Localized user-driven topic discovery via boosted ensemble of nonnegative matrix factorizationKnowledge and Information Systems10.1007/s10115-017-1147-956:3(503-531)Online publication date: 1-Sep-2018
https://dl.acm.org/doi/10.1007/s10115-017-1147-9
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