research-article

FORM: Follow the Online Regularized Meta-Leader for Cold-Start Recommendation

Authors:

Guihai ChenAuthors Info & Claims

SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

Pages 1177 - 1186

https://doi.org/10.1145/3404835.3462831

Published: 11 July 2021 Publication History

Abstract

Meta-learning based recommendation systems alleviate the cold-start problem through a bi-level meta-optimization process. Recommendation borrows prior experience from pre-trained static system-level parameters and fine-tunes the model in user-level for new users. However, it is more natural for the system to sample users in a dynamic online sequence in most real-world recommendation systems, which brings further challenges for existing meta-learning based recommendation: system-level updates begins before user-level recommendation models have converged on the whole time series; stable and randomness-resistant bi-level gradient descent approaches are missing in the current meta-learning framework; evaluation on learning abilities across different users are lacked for exploring the diversities of different users.

In this paper, we propose an online regularized meta-leader recommendation approaches named FORM to address such problems. To transfer meta-learning based recommender into the online scenario, we develop follow-the-meta-leader algorithm to learn stable online gradients. Regularized methods are then introduced to alleviate the volatility of online systems and produce sparse weight parameters. Besides, we design a scalable meta-trained learning rate based on the variance and learning-shots of existing users to guide the model to adapt efficiently to new users. Extensive experiments on three public datasets and one commercial online advertisement dataset demonstrate our approaches' effectiveness and stability, which outperform other state-of-the-art methods and achieve a stable and fast adaption on new users.

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Cited By

Balasubramanian KAlshabanah AMarkowitz EVer Steeg GAnnavaram M(2024)Biased User History Synthesis for Personalized Long-Tail Item RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688141(189-199)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688141
Weimann KConrad T(2024)FELRec: efficient handling of item cold-start with dynamic representation learning in recommender systemsInternational Journal of Data Science and Analytics10.1007/s41060-024-00635-5Online publication date: 7-Oct-2024
https://doi.org/10.1007/s41060-024-00635-5
Wang CZhu YLiu HZang TWang KYu J(2023)Multifaceted Relation-aware Meta-learning with Dual Customization for User Cold-start RecommendationACM Transactions on Knowledge Discovery from Data10.1145/359745817:9(1-27)Online publication date: 18-Jul-2023
https://dl.acm.org/doi/10.1145/3597458
Show More Cited By

Index Terms

FORM: Follow the Online Regularized Meta-Leader for Cold-Start Recommendation
1. Information systems
  1. Information retrieval
    1. Users and interactive retrieval
      1. Personalization

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Published In

cover image ACM Conferences

SIGIR '21: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 2021

2998 pages

ISBN:9781450380379

DOI:10.1145/3404835

General Chairs:
Fernando Diaz
(Google)
,
Chirag Shah
University of Washington
,
Torsten Suel
New York University
,
Program Chairs:
Pablo Castells
Universidad Autónoma de Madrid, Amazon
,
Rosie Jones
Spotify
,
Tetsuya Sakai
Waseda University

Copyright © 2021 ACM.

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SIGIR: ACM Special Interest Group on Information Retrieval

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New York, NY, United States

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Published: 11 July 2021

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National Key R&D Program of China
National Natural Science Foundation of China

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SIGIR '21

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SIGIR

SIGIR '21: The 44th International ACM SIGIR Conference on Research and Development in Information Retrieval

July 11 - 15, 2021

Virtual Event, Canada

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

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Cited By

Balasubramanian KAlshabanah AMarkowitz EVer Steeg GAnnavaram M(2024)Biased User History Synthesis for Personalized Long-Tail Item RecommendationProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688141(189-199)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688141
Weimann KConrad T(2024)FELRec: efficient handling of item cold-start with dynamic representation learning in recommender systemsInternational Journal of Data Science and Analytics10.1007/s41060-024-00635-5Online publication date: 7-Oct-2024
https://doi.org/10.1007/s41060-024-00635-5
Wang CZhu YLiu HZang TWang KYu J(2023)Multifaceted Relation-aware Meta-learning with Dual Customization for User Cold-start RecommendationACM Transactions on Knowledge Discovery from Data10.1145/359745817:9(1-27)Online publication date: 18-Jul-2023
https://dl.acm.org/doi/10.1145/3597458
Wang WLin XWang LFeng FWei YChua TEl Saddik AMei TCucchiara RBertini MTobon Vallejo DAtrey PHossain M(2023)Equivariant Learning for Out-of-Distribution Cold-start RecommendationProceedings of the 31st ACM International Conference on Multimedia10.1145/3581783.3612522(903-914)Online publication date: 27-Oct-2023
https://doi.org/10.1145/3581783.3612522
Wang CZhu YSun AWang ZWang KChen HDuh WHuang HKato MMothe JPoblete B(2023)A Preference Learning Decoupling Framework for User Cold-Start RecommendationProceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3539618.3591627(1168-1177)Online publication date: 19-Jul-2023
https://dl.acm.org/doi/10.1145/3539618.3591627
Xu YWang EYang YXiong H(2023)GS-RS: A Generative Approach for Alleviating Cold Start and Filter Bubbles in Recommender SystemsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.329014036:2(668-681)Online publication date: 28-Jun-2023
https://dl.acm.org/doi/10.1109/TKDE.2023.3290140
Zhu CDu PZhang WYu YCao YZhang ARangwala H(2022)Combo-Fashion: Fashion Clothes Matching CTR Prediction with Item HistoryProceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3534678.3539101(4621-4629)Online publication date: 14-Aug-2022
https://dl.acm.org/doi/10.1145/3534678.3539101
Liu HJing LYu DZhou MNg MMagalhães Jdel Bimbo ASatoh SSebe NAlameda-Pineda XJin QOria VToni L(2022)Learning Intrinsic and Extrinsic Intentions for Cold-start Recommendation with Neural Stochastic ProcessesProceedings of the 30th ACM International Conference on Multimedia10.1145/3503161.3548302(491-500)Online publication date: 10-Oct-2022
https://dl.acm.org/doi/10.1145/3503161.3548302
Pang HGiunchiglia FLi XGuan RFeng X(2022)PNMTA: A Pretrained Network Modulation and Task Adaptation Approach for User Cold-Start RecommendationProceedings of the ACM Web Conference 202210.1145/3485447.3511963(348-359)Online publication date: 25-Apr-2022
https://dl.acm.org/doi/10.1145/3485447.3511963
Guan RPang HGiunchiglia FLi XYang XFeng XAmigo ECastells PGonzalo JCarterette BCulpepper JKazai G(2022)Deployable and Continuable Meta-learning-Based Recommender System with Fast User-Incremental UpdatesProceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval10.1145/3477495.3531964(1423-1433)Online publication date: 6-Jul-2022
https://dl.acm.org/doi/10.1145/3477495.3531964
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