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Local Collaborative Autoencoders

Authors:

Jongwuk LeeAuthors Info & Claims

WSDM '21: Proceedings of the 14th ACM International Conference on Web Search and Data Mining

Pages 734 - 742

https://doi.org/10.1145/3437963.3441808

Published: 08 March 2021 Publication History

Abstract

This work presents a generalized local factor model, namely Local Collaborative Autoencoders (LOCA). To our knowledge, it is the first generalized framework under the local low-rank assumption that builds on the neural recommendation models. We explore a large number of local models by adopting a generalized framework with different weight schemes for training and aggregating them. Besides, we develop a novel method of discovering a sub-community to maximize the coverage of local models. Our experimental results demonstrate that LOCA is highly scalable, achieving state-of-the-art results by outperforming existing AE-based and local latent factor models on several large-scale public benchmarks.

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Cited By

Pan JCaverlee JZhu ZNejdl WAuer SKarras OCha MMoens MNajork M(2025)Combating Heterogeneous Model Biases in Recommendations via BoostingProceedings of the Eighteenth ACM International Conference on Web Search and Data Mining10.1145/3701551.3703505(222-231)Online publication date: 10-Mar-2025
https://dl.acm.org/doi/10.1145/3701551.3703505
Liu WLiang SZhu HHou U LYu JLi XYin J(2025)Variational Kernel Density Estimation Recommendation Algorithm for Users with Diverse Activity LevelsDatabase Systems for Advanced Applications10.1007/978-981-97-5779-4_1(3-18)Online publication date: 11-Jan-2025
https://doi.org/10.1007/978-981-97-5779-4_1
Liu WU LLiang SZhu HYu JLiu YYin J(2024)VAE*: A Novel Variational Autoencoder via Revisiting Positive and Negative Samples for Top-N RecommendationACM Transactions on Knowledge Discovery from Data10.1145/368055218:9(1-24)Online publication date: 24-Oct-2024
https://dl.acm.org/doi/10.1145/3680552
Show More Cited By

Index Terms

Local Collaborative Autoencoders
1. Information systems
  1. Information systems applications
    1. Data mining
      1. Collaborative filtering
    2. Decision support systems
      1. Expert systems

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Published In

cover image ACM Conferences

WSDM '21: Proceedings of the 14th ACM International Conference on Web Search and Data Mining

March 2021

1192 pages

ISBN:9781450382977

DOI:10.1145/3437963

General Chairs:
Liane Lewin-Eytan
Amazon, Israel
,
David Carmel
Amazon, Israel
,
Elad Yom-Tov
Microsoft, Israel
,
Program Chairs:
Eugene Agichtein
Emory University and Amazon, USA
,
Evgeniy Gabrilovich
Google Health, USA

Copyright © 2021 ACM.

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Published: 08 March 2021

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AI Graduate School Support Program
National Research Foundation of Korea
ICT Creative Consilience Program

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WSDM '21

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WSDM '21: The Fourteenth ACM International Conference on Web Search and Data Mining

March 8 - 12, 2021

Virtual Event, Israel

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Overall Acceptance Rate 498 of 2,863 submissions, 17%

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Cited By

Pan JCaverlee JZhu ZNejdl WAuer SKarras OCha MMoens MNajork M(2025)Combating Heterogeneous Model Biases in Recommendations via BoostingProceedings of the Eighteenth ACM International Conference on Web Search and Data Mining10.1145/3701551.3703505(222-231)Online publication date: 10-Mar-2025
https://dl.acm.org/doi/10.1145/3701551.3703505
Liu WLiang SZhu HHou U LYu JLi XYin J(2025)Variational Kernel Density Estimation Recommendation Algorithm for Users with Diverse Activity LevelsDatabase Systems for Advanced Applications10.1007/978-981-97-5779-4_1(3-18)Online publication date: 11-Jan-2025
https://doi.org/10.1007/978-981-97-5779-4_1
Liu WU LLiang SZhu HYu JLiu YYin J(2024)VAE*: A Novel Variational Autoencoder via Revisiting Positive and Negative Samples for Top-N RecommendationACM Transactions on Knowledge Discovery from Data10.1145/368055218:9(1-24)Online publication date: 24-Oct-2024
https://dl.acm.org/doi/10.1145/3680552
Liang SPan Zliu wYin Jde Rijke M(2024)A Survey on Variational Autoencoders in Recommender SystemsACM Computing Surveys10.1145/366336456:10(1-40)Online publication date: 24-Jun-2024
https://dl.acm.org/doi/10.1145/3663364
Pan JZhu ZWang JLin ACaverlee J(2024)Countering Mainstream Bias via End-to-End Adaptive Local LearningAdvances in Information Retrieval10.1007/978-3-031-56069-9_6(75-89)Online publication date: 23-Mar-2024
https://doi.org/10.1007/978-3-031-56069-9_6
Baccarelli EScarpiniti MMomenzadeh A(2023)Twinned Residual Auto-Encoder (TRAE)—A new DL architecture for denoising super-resolution and task-aware feature learning from COVID-19 CT imagesExpert Systems with Applications10.1016/j.eswa.2023.120104225(120104)Online publication date: Sep-2023
https://doi.org/10.1016/j.eswa.2023.120104
Vančura VAlves RKasalický PKordík P(2022)Scalable Linear Shallow Autoencoder for Collaborative FilteringProceedings of the 16th ACM Conference on Recommender Systems10.1145/3523227.3551482(604-609)Online publication date: 12-Sep-2022
https://dl.acm.org/doi/10.1145/3523227.3551482
Choi MKim JLee JShim HLee JSelcuk Candan KLiu HAkoglu LLuna Dong XTang J(2022)S-WalkProceedings of the Fifteenth ACM International Conference on Web Search and Data Mining10.1145/3488560.3498464(150-160)Online publication date: 11-Feb-2022
https://dl.acm.org/doi/10.1145/3488560.3498464
Ma WChen XPan WMing ZSelcuk Candan KLiu HAkoglu LLuna Dong XTang J(2022)VAE++Proceedings of the Fifteenth ACM International Conference on Web Search and Data Mining10.1145/3488560.3498436(666-674)Online publication date: 11-Feb-2022
https://dl.acm.org/doi/10.1145/3488560.3498436
Zhu ZCaverlee JSelcuk Candan KLiu HAkoglu LLuna Dong XTang J(2022)Fighting Mainstream Bias in Recommender Systems via Local Fine TuningProceedings of the Fifteenth ACM International Conference on Web Search and Data Mining10.1145/3488560.3498427(1497-1506)Online publication date: 11-Feb-2022
https://dl.acm.org/doi/10.1145/3488560.3498427
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