short-paper

ReLoop: A Self-Correction Continual Learning Loop for Recommender Systems

Authors:

Rui ZhangAuthors Info & Claims

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 2692 - 2697

https://doi.org/10.1145/3477495.3531922

Published: 07 July 2022 Publication History

Abstract

Deep learning-based recommendation has become a widely adopted technique in various online applications. Typically, a deployed model undergoes frequent re-training to capture users' dynamic behaviors from newly collected interaction logs. However, the current model training process only acquires users' feedbacks as labels, but fails to take into account the errors made in previous recommendations. Inspired by the intuition that humans usually reflect and learn from mistakes, in this paper, we attempt to build a self-correction continual learning loop (dubbed ReLoop) for recommender systems. In particular, a new customized loss is employed to encourage every new model version to reduce prediction errors over the previous model version during training. Our ReLoop learning framework enables a continual self-correction process in the long run and thus is expected to obtain better performance over existing training strategies. Both offline experiments and an online A/B test have been conducted to validate the effectiveness of ReLoop.

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Cited By

Tang HWu SSun XZeng JXu GLi Q(2024)TCGC: Temporal Collaboration-Aware Graph Co-Evolution Learning for Dynamic RecommendationACM Transactions on Information Systems10.1145/368747043:1(1-27)Online publication date: 26-Nov-2024
https://dl.acm.org/doi/10.1145/3687470
Deng YWang GYue SRao WZu QWang WChen SRen JZhang Y(2024)RelayRec: Empowering Privacy-Preserving CTR Prediction via Cloud-Device Relay Learning2024 23rd ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)10.1109/IPSN61024.2024.00020(188-199)Online publication date: 13-May-2024
https://doi.org/10.1109/IPSN61024.2024.00020
Yuan HHe XHu RWang ZZhou JFeng YGong L(2024)GCPN: A Group Connected based Method for Continual Vertical Federated Recommender Systems in Data Ecosystems2024 IEEE International Conference on Web Services (ICWS)10.1109/ICWS62655.2024.00022(35-44)Online publication date: 7-Jul-2024
https://doi.org/10.1109/ICWS62655.2024.00022
Show More Cited By

Index Terms

ReLoop: A Self-Correction Continual Learning Loop for Recommender Systems
1. Information systems
  1. Information systems applications
    1. Computational advertising

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cover image ACM Conferences

SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2022

3569 pages

ISBN:9781450387323

DOI:10.1145/3477495

General Chairs:
Enrique Amigo
UNED
,
Pablo Castells
UAM and Amazon
,
Julio Gonzalo
UNED
,
Program Chairs:
Ben Carterette
Spotify
,
J. Shane Culpepper
RMIT University
,
Gabriella Kazai
Waseda University

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Published: 07 July 2022

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SIGIR '22: The 45th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2022

Madrid, Spain

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Cited By

Tang HWu SSun XZeng JXu GLi Q(2024)TCGC: Temporal Collaboration-Aware Graph Co-Evolution Learning for Dynamic RecommendationACM Transactions on Information Systems10.1145/368747043:1(1-27)Online publication date: 26-Nov-2024
https://dl.acm.org/doi/10.1145/3687470
Deng YWang GYue SRao WZu QWang WChen SRen JZhang Y(2024)RelayRec: Empowering Privacy-Preserving CTR Prediction via Cloud-Device Relay Learning2024 23rd ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)10.1109/IPSN61024.2024.00020(188-199)Online publication date: 13-May-2024
https://doi.org/10.1109/IPSN61024.2024.00020
Yuan HHe XHu RWang ZZhou JFeng YGong L(2024)GCPN: A Group Connected based Method for Continual Vertical Federated Recommender Systems in Data Ecosystems2024 IEEE International Conference on Web Services (ICWS)10.1109/ICWS62655.2024.00022(35-44)Online publication date: 7-Jul-2024
https://doi.org/10.1109/ICWS62655.2024.00022
Schifferer BShi WMoreira GOldridge EDeotte CTitericz GOnodera KDhinwa PAgrawal VGreen C(2023)Investigating the effects of incremental training on neural ranking modelsProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608872(318-321)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608872
Yang CChen JYu QWu XMa KZhao ZFang ZChen WFan CHe JPeng CLin ZShao JFrommholz IHopfgartner FLee MOakes MLalmas MZhang MSantos R(2023)An Incremental Update Framework for Online Recommenders with Data-Driven PriorProceedings of the 32nd ACM International Conference on Information and Knowledge Management10.1145/3583780.3615456(4894-4900)Online publication date: 21-Oct-2023
https://dl.acm.org/doi/10.1145/3583780.3615456
Zhu JCai GHuang JDong ZTang RZhang WSingh ASun YAkoglu LGunopulos DYan XKumar ROzcan FYe J(2023)ReLoop2: Building Self-Adaptive Recommendation Models via Responsive Error Compensation LoopProceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3580305.3599785(5728-5738)Online publication date: 6-Aug-2023
https://dl.acm.org/doi/10.1145/3580305.3599785
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
https://dl.acm.org/doi/10.1145/3543507.3583237
Durmuş FTürkmen BAldemir MTürksoy R(2023)Capturing Dynamic User Behavior in Recommender Systems by Finetuning2023 8th International Conference on Computer Science and Engineering (UBMK)10.1109/UBMK59864.2023.10286678(509-514)Online publication date: 13-Sep-2023
https://doi.org/10.1109/UBMK59864.2023.10286678
Yao YZhou RZhang WGou LMa H(2023)Continual Learning with Ranking Self-Correction in Event Detection2023 9th International Conference on Computer and Communications (ICCC)10.1109/ICCC59590.2023.10507480(2222-2226)Online publication date: 8-Dec-2023
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Dai QLv YZhu JYe JDong ZZhang RXia STang RAl Hasan MXiong L(2022)LCD: Adaptive Label Correction for Denoising Music RecommendationProceedings of the 31st ACM International Conference on Information & Knowledge Management10.1145/3511808.3557625(3903-3907)Online publication date: 17-Oct-2022
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