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Online-updating regularized kernel matrix factorization models for large-scale recommender systems | Proceedings of the 2008 ACM conference on Recommender systems

poster

Online-updating regularized kernel matrix factorization models for large-scale recommender systems

Authors:

Steffen Rendle,

Lars Schmidt-ThiemeAuthors Info & Claims

RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems

Pages 251 - 258

https://doi.org/10.1145/1454008.1454047

Published: 23 October 2008 Publication History

Abstract

Regularized matrix factorization models are known to generate high quality rating predictions for recommender systems. One of the major drawbacks of matrix factorization is that once computed, the model is static. For real-world applications dynamic updating a model is one of the most important tasks. Especially when ratings on new users or new items come in, updating the feature matrices is crucial.

In this paper, we generalize regularized matrix factorization (RMF) to regularized kernel matrix factorization (RKMF). Kernels provide a flexible method for deriving new matrix factorization methods. Furthermore with kernels nonlinear interactions between feature vectors are possible. We propose a generic method for learning RKMF models. From this method we derive an online-update algorithm for RKMF models that allows to solve the new-user/new-item problem. Our evaluation indicates that our proposed online-update methods are accurate in approximating a full retrain of a RKMF model while the runtime of online-updating is in the range of milliseconds even for huge datasets like Netflix.

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Cited By

Wang RHuang JZhang YLi JWang YZhao WLiu SMendis CAbdelzaher THui Yang GWang HHan SHauff CZuccon GZhang Y(2024)TGOnline: Enhancing Temporal Graph Learning with Adaptive Online Meta-LearningProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657791(1659-1669)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657791
Zhang PYan YZhang XKang LLi CHuang FWang SKim SHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)GPT4Rec: Graph Prompt Tuning for Streaming RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657720(1774-1784)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657720
Yuan JWoon WCoba L(2023)Adversarial Sleeping Bandit Problems with Multiple Plays: Algorithm and Ranking ApplicationProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608824(744-749)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608824
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Index Terms

Online-updating regularized kernel matrix factorization models for large-scale recommender systems
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
    2. Machine learning approaches
      1. Markov decision processes
2. Theory of computation
  1. Theory and algorithms for application domains
    1. Machine learning theory
      1. Markov decision processes

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Published In

cover image ACM Conferences

RecSys '08: Proceedings of the 2008 ACM conference on Recommender systems

October 2008

348 pages

ISBN:9781605580937

DOI:10.1145/1454008

General Chair:
Pearl Pu
Ecole Polytechnique Fédérale de Lausanne (EPFL), Switzerland
,
Program Chairs:
Derek Bridge
University College Cork, Ireland
,
Bamshad Mobasher
DePaul University, USA
,
Francesco Ricci
Free University Bozen-Bolzano, Italy

Copyright © 2008 ACM.

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Publication History

Published: 23 October 2008

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RecSys08

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RecSys08: ACM Conference on Recommender Systems

October 23 - 25, 2008

Lausanne, Switzerland

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

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RecSys '24

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sigchi

18th ACM Conference on Recommender Systems

October 14 - 18, 2024

Bari , Italy

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Cited By

Wang RHuang JZhang YLi JWang YZhao WLiu SMendis CAbdelzaher THui Yang GWang HHan SHauff CZuccon GZhang Y(2024)TGOnline: Enhancing Temporal Graph Learning with Adaptive Online Meta-LearningProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657791(1659-1669)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657791
Zhang PYan YZhang XKang LLi CHuang FWang SKim SHui Yang GWang HHan SHauff CZuccon GZhang Y(2024)GPT4Rec: Graph Prompt Tuning for Streaming RecommendationProceedings of the 47th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3626772.3657720(1774-1784)Online publication date: 10-Jul-2024
https://dl.acm.org/doi/10.1145/3626772.3657720
Yuan JWoon WCoba L(2023)Adversarial Sleeping Bandit Problems with Multiple Plays: Algorithm and Ranking ApplicationProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608824(744-749)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608824
He BHe XZhang YTang RMa C(2023)Dynamically Expandable Graph Convolution for Streaming RecommendationProceedings of the ACM Web Conference 202310.1145/3543507.3583237(1457-1467)Online publication date: 30-Apr-2023
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Tong SLiu JHong YHuang ZWu LLiu QHuang WChen EZhang DZhang ARangwala H(2022)Incremental Cognitive Diagnosis for Intelligent EducationProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539399(1760-1770)Online publication date: 14-Aug-2022
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