research-article

A Generic Coordinate Descent Framework for Learning from Implicit Feedback

Authors:

Immanuel Bayer,

Bhargav Kanagal,

Steffen RendleAuthors Info & Claims

WWW '17: Proceedings of the 26th International Conference on World Wide Web

Pages 1341 - 1350

https://doi.org/10.1145/3038912.3052694

Published: 03 April 2017 Publication History

Abstract

In recent years, interest in recommender research has shifted from explicit feedback towards implicit feedback data. A diversity of complex models has been proposed for a wide variety of applications. Despite this, learning from implicit feedback is still computationally challenging. So far, most work relies on stochastic gradient descent (SGD) solvers which are easy to derive, but in practice challenging to apply, especially for tasks with many items. For the simple matrix factorization model, an efficient coordinate descent (CD) solver has been previously proposed. However, efficient CD approaches have not been derived for more complex models.

In this paper, we provide a new framework for deriving efficient CD algorithms for complex recommender models. We identify and introduce the property of k-separable models. We show that k-separability is a sufficient property to allow efficient optimization of implicit recommender problems with CD. We illustrate this framework on a variety of state-of-the-art models including factorization machines and Tucker decomposition. To summarize, our work provides the theory and building blocks to derive efficient implicit CD algorithms for complex recommender models.

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Cited By

Dai WWang Y(2024)Web Semantic-Based Robust Graph Contrastive Learning for Recommendation via Invariant LearningInternational Journal on Semantic Web and Information Systems10.4018/IJSWIS.33796220:1(1-15)Online publication date: 14-Feb-2024
https://doi.org/10.4018/IJSWIS.337962
Togashi ROka TOhsaka NMorimura T(2024)Safe Collaborative FilteringSSRN Electronic Journal10.2139/ssrn.4767721Online publication date: 2024
https://doi.org/10.2139/ssrn.4767721
De Pauw JGoethals B(2024)The Role of Unknown Interactions in Implicit Matrix Factorization — A Probabilistic ViewProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688100(219-227)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688100
Show More Cited By

Index Terms

A Generic Coordinate Descent Framework for Learning from Implicit Feedback
1. Computing methodologies
  1. Machine learning
    1. Learning settings
      1. Learning from implicit feedback
    2. Machine learning approaches
      1. Factorization methods
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Other conferences

WWW '17: Proceedings of the 26th International Conference on World Wide Web

April 2017

1678 pages

ISBN:9781450349130

General Chairs:
Rick Barrett
W3Events
,
Rick Cummings
Murdoch University
,
Program Chairs:
Eugene Agichtein
Emory University
,
Evgeniy Gabrilovich
Google Research

Copyright © 2017 Copyright is held by the International World Wide Web Conference Committee (IW3C2).

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SIGWEB: ACM Special Interest Group on Hypertext, Hypermedia, and Web

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International World Wide Web Conferences Steering Committee

Republic and Canton of Geneva, Switzerland

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Published: 03 April 2017

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WWW '17

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WWW '17: 26th International World Wide Web Conference

April 3 - 7, 2017

Perth, Australia

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WWW '17 Paper Acceptance Rate 164 of 966 submissions, 17%;

Overall Acceptance Rate 1,899 of 8,196 submissions, 23%

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Cited By

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https://doi.org/10.4018/IJSWIS.337962
Togashi ROka TOhsaka NMorimura T(2024)Safe Collaborative FilteringSSRN Electronic Journal10.2139/ssrn.4767721Online publication date: 2024
https://doi.org/10.2139/ssrn.4767721
De Pauw JGoethals B(2024)The Role of Unknown Interactions in Implicit Matrix Factorization — A Probabilistic ViewProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688100(219-227)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688100
Wu LXia YMin SXia Z(2024)Deep Attentive Interest Collaborative Filtering for Recommender SystemsIEEE Transactions on Emerging Topics in Computing10.1109/TETC.2023.328640412:2(467-481)Online publication date: Apr-2024
https://doi.org/10.1109/TETC.2023.3286404
Qin BHuang ZWu ZWang CChen Y(2024)MetaGA: Metalearning With Graph-Attention for Improved Long-Tail Item RecommendationIEEE Transactions on Computational Social Systems10.1109/TCSS.2024.341104311:5(6544-6556)Online publication date: Oct-2024
https://doi.org/10.1109/TCSS.2024.3411043
Sugahara KOkamoto K(2024)Hierarchical matrix factorization for interpretable collaborative filteringPattern Recognition Letters10.1016/j.patrec.2024.03.003180(99-106)Online publication date: Apr-2024
https://doi.org/10.1016/j.patrec.2024.03.003
Xu JHuang JZhao JYang J(2024)HyNCFExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121875238:PBOnline publication date: 27-Feb-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121875
Sun SCai TYan FJu S(2024)Two-Stage Enhancement for Recommendation Systems Based on Contrastive LearningWeb Information Systems and Applications10.1007/978-981-97-7707-5_13(150-162)Online publication date: 11-Sep-2024
https://doi.org/10.1007/978-981-97-7707-5_13
Zhang ASheng LCai ZWang XChua TOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Empowering collaborative filtering with principled adversarial contrastive lossProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3666395(6242-6266)Online publication date: 10-Dec-2023
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Zhu JLi KPeng JQi J(2023)Self-Supervised Graph Attention Collaborative Filtering for RecommendationElectronics10.3390/electronics1204079312:4(793)Online publication date: 5-Feb-2023
https://doi.org/10.3390/electronics12040793
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