short-paper

Scalable Linear Shallow Autoencoder for Collaborative Filtering

Authors:

Vojtěch Vančura,

Petr Kasalický,

Pavel KordíkAuthors Info & Claims

RecSys '22: Proceedings of the 16th ACM Conference on Recommender Systems

Pages 604 - 609

https://doi.org/10.1145/3523227.3551482

Published: 13 September 2022 Publication History

Abstract

Recently, the RS research community has witnessed a surge in popularity for shallow autoencoder-based CF methods. Due to its straightforward implementation and high accuracy on item retrieval metrics, EASE is potentially the most prominent of these models. Despite its accuracy and simplicity, EASE cannot be employed in some real-world recommender system applications due to its inability to scale to huge interaction matrices. In this paper, we proposed ELSA, a scalable shallow autoencoder method for implicit feedback recommenders. ELSA is a scalable autoencoder in which the hidden layer is factorizable into a low-rank plus sparse structure, thereby drastically lowering memory consumption and computation time. We conducted a comprehensive offline experimental section that combined synthetic and several real-world datasets. We also validated our strategy in an online setting by comparing ELSA to baselines in a live recommender system using an A/B test. Experiments demonstrate that ELSA is scalable and has competitive performance. Finally, we demonstrate the explainability of ELSA by illustrating the recovered latent space.

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Cited By

Vančura VKordík PStraka M(2024)beeFormer: Bridging the Gap Between Semantic and Interaction Similarity in Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3691707(1102-1107)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3691707
Spišák MBartyzal RHoskovec APeška L(2024)On Interpretability of Linear AutoencodersProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688179(975-980)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688179
Ai JCai YSu ZPeng DZhao F(2024)Measuring similarity based on user activeness in recommender systems to improve algorithm scalabilityEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106842126:PBOnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.engappai.2023.106842
Show More Cited By

Index Terms

Scalable Linear Shallow Autoencoder for Collaborative Filtering
1. Computing methodologies
  1. Artificial intelligence
  2. Machine learning
    1. Machine learning approaches
      1. Learning linear models
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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RecSys '22: Proceedings of the 16th ACM Conference on Recommender Systems

September 2022

743 pages

ISBN:9781450392785

DOI:10.1145/3523227

Copyright © 2022 ACM.

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Published: 13 September 2022

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Grant Agency of the Czech Technical University

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RecSys '22

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RecSys '22: Sixteenth ACM Conference on Recommender Systems

September 18 - 23, 2022

WA, Seattle, USA

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

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Cited By

Vančura VKordík PStraka M(2024)beeFormer: Bridging the Gap Between Semantic and Interaction Similarity in Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3691707(1102-1107)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3691707
Spišák MBartyzal RHoskovec APeška L(2024)On Interpretability of Linear AutoencodersProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688179(975-980)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688179
Ai JCai YSu ZPeng DZhao F(2024)Measuring similarity based on user activeness in recommender systems to improve algorithm scalabilityEngineering Applications of Artificial Intelligence10.1016/j.engappai.2023.106842126:PBOnline publication date: 1-Feb-2024
https://dl.acm.org/doi/10.1016/j.engappai.2023.106842
Kannout EGrodzki MGrzegorowski M(2023)Towards addressing item cold-start problem in collaborative filtering by embedding agglomerative clustering and FP-growth into the recommendation systemComputer Science and Information Systems10.2298/CSIS221116052K20:4(1343-1366)Online publication date: 2023
https://doi.org/10.2298/CSIS221116052K
Kasalicky PLedent AAlves R(2023)Uncertainty-adjusted Inductive Matrix Completion with Graph Neural NetworksProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3610654(1169-1174)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3610654
Spišák MBartyzal RHoskovec APeska LTůma M(2023)Scalable Approximate NonSymmetric Autoencoder for Collaborative FilteringProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608827(763-770)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608827
Vančura V(2023)Scalable and Explainable Linear Shallow Autoencoders for Collaborative Filtering from Industrial PerspectiveProceedings of the 31st ACM Conference on User Modeling, Adaptation and Personalization10.1145/3565472.3595630(290-295)Online publication date: 18-Jun-2023
https://dl.acm.org/doi/10.1145/3565472.3595630
Moon JKim HLee JChen HDuh WHuang HKato MMothe JPoblete B(2023)It's Enough: Relaxing Diagonal Constraints in Linear Autoencoders for RecommendationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591704(1639-1648)Online publication date: 19-Jul-2023
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An ZTan YZhang JJiang ZLi C(2023)A Session Recommendation Model Based on Heterogeneous Graph Neural NetworkKnowledge Science, Engineering and Management10.1007/978-3-031-40289-0_13(160-171)Online publication date: 16-Aug-2023
https://dl.acm.org/doi/10.1007/978-3-031-40289-0_13

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