research-article

A Gain-Tuning Dynamic Negative Sampler for Recommendation

Authors:

Zhicheng DouAuthors Info & Claims

WWW '22: Proceedings of the ACM Web Conference 2022

Pages 277 - 285

https://doi.org/10.1145/3485447.3511956

Published: 25 April 2022 Publication History

Abstract

Selecting reliable negative training instances is the challenging task in the implicit feedback-based recommendation, which is optimized by pairwise learning on user feedback data. The existing methods usually exploit various negative samplers (i.e., heuristic-based or GAN-based sampling) on user feedback data to improve the quality of negative samples. However, these methods usually focused on maintaining the hard negative samples with a high gradient for training, causing the false negative samples to be selected preferentially. The limitation of the false negative noise amplification may lead to overfitting and further poor generalization of the model. To address this issue, we propose a Gain-Tuning Dynamic Negative Sampling GDNS to make the recommendation more robust and effective. Our proposed model designs an expectational gain sampler, concerning the expectation of user’ preference gap between the positive and negative samples in training, to guide the negative selection dynamically. This gain-tuning negative sampler can effectively identify the false negative samples and further diminish the risk of introducing false negative instances. Moreover, for improving the training efficiency, we construct positive and negative groups for each user in each iteration, and develop a group-wise optimizer to optimize them in a cross manner. Experiments on two real-world datasets show our approach significantly outperforms state-of-the-art negative sampling baselines.

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Cited By

Bae HKim YKim HKim SChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Negative Sampling in Next-POI Recommendations: Observation, Approach, and EvaluationProceedings of the ACM Web Conference 202410.1145/3589334.3645681(3888-3899)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645681
Chen RFan JWu M(2024)ABNSExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.123868250:COnline publication date: 15-Sep-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.123868
Zhao YChen RLai RHan QSong HChen L(2023)Augmented Negative Sampling for Collaborative FilteringProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608811(256-266)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608811
Show More Cited By

Index Terms

A Gain-Tuning Dynamic Negative Sampler for Recommendation
1. Computing methodologies
  1. Machine learning
2. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

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Published In

cover image ACM Conferences

WWW '22: Proceedings of the ACM Web Conference 2022

April 2022

3764 pages

ISBN:9781450390965

DOI:10.1145/3485447

Editors:
Frédérique Laforest
INSA Lyon, France
,
Raphaël Troncy
EURECOM, France
,
Elena Simperl
King’s College London, UK
,
Deepak Agarwal
Pinterest, USA
,
Aristides Gionis
KTH Royal Institute of Technology, Sweden
,
Ivan Herman
W3C / retired
,
Lionel Médini
Université Lyon 1, France

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Published: 25 April 2022

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Cited By

Bae HKim YKim HKim SChua TNgo CKa-Wei Lee RKumar RLauw H(2024)Negative Sampling in Next-POI Recommendations: Observation, Approach, and EvaluationProceedings of the ACM Web Conference 202410.1145/3589334.3645681(3888-3899)Online publication date: 13-May-2024
https://dl.acm.org/doi/10.1145/3589334.3645681
Chen RFan JWu M(2024)ABNSExpert Systems with Applications: An International Journal10.1016/j.eswa.2024.123868250:COnline publication date: 15-Sep-2024
https://dl.acm.org/doi/10.1016/j.eswa.2024.123868
Zhao YChen RLai RHan QSong HChen L(2023)Augmented Negative Sampling for Collaborative FilteringProceedings of the 17th ACM Conference on Recommender Systems10.1145/3604915.3608811(256-266)Online publication date: 14-Sep-2023
https://dl.acm.org/doi/10.1145/3604915.3608811
Chen XTan DGupta PBromuri S(2023)Pair-wise selective classification with dynamic sampling for shipment importer predictionProceedings of the 2023 15th International Conference on Machine Learning and Computing10.1145/3587716.3587741(152-157)Online publication date: 17-Feb-2023
https://dl.acm.org/doi/10.1145/3587716.3587741
Lai RChen LZhao YChen RHan QChua TLauw HSi LTerzi ETsaparas P(2023)Disentangled Negative Sampling for Collaborative FilteringProceedings of the Sixteenth ACM International Conference on Web Search and Data Mining10.1145/3539597.3570419(96-104)Online publication date: 27-Feb-2023
https://dl.acm.org/doi/10.1145/3539597.3570419
Li YLian DChen J(2023)Cache-Enhanced InBatch Sampling with Difficulty-Based Replacement Strategies for Learning RecommendersDatabase Systems for Advanced Applications. DASFAA 2023 International Workshops10.1007/978-3-031-35415-1_7(95-108)Online publication date: 17-Apr-2023
https://dl.acm.org/doi/10.1007/978-3-031-35415-1_7
Chen JLian DLi YWang BZheng KChen EKoyejo SMohamed SAgarwal ABelgrave DCho KOh A(2022)Cache-augmented inbatch importance resampling for training recommender retrieverProceedings of the 36th International Conference on Neural Information Processing Systems10.5555/3600270.3602793(34817-34830)Online publication date: 28-Nov-2022
https://dl.acm.org/doi/10.5555/3600270.3602793

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