research-article

Exploring False Hard Negative Sample in Cross-Domain Recommendation

Authors:

Jie ZhouAuthors Info & Claims

RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems

Pages 502 - 514

https://doi.org/10.1145/3604915.3608791

Published: 14 September 2023 Publication History

Abstract

Negative Sampling in recommendation aims to capture informative negative instances for the sparse user-item interactions to improve the performance. Conventional negative sampling methods tend to select informative hard negative samples (HNS) besides the default random samples. However, these hard negative sampling methods usually struggle with false hard negative samples (FHNS), which happens when a user-item interaction has not been observed yet and is picked as a negative sample, while the user will actually interact with this item once exposed to it. Such FHNS issues may seriously confuse the model training, while most conventional hard negative sampling methods do not systematically explore and distinguish FHNS from HNS. To address this issue, we propose a novel model-agnostic Real Hard Negative Sampling (RealHNS) framework specially for cross-domain recommendation (CDR), which aims to discover the false and refine the real from all HNS via both general and cross-domain real hard negative sample selectors. For the general part, we conduct the coarse- and fine-grained real HNS selectors sequentially, armed with a dynamic item-based FHNS filter to find high-quality HNS. For the cross-domain part, we further design a new cross-domain HNS for alleviating negative transfer in CDR and discover its corresponding FHNS via a dynamic user-based FHNS filter to keep its power. We conduct experiments on four datasets based on three representative hard negative sampling methods, along with extensive model analyses, ablation studies, and universality analyses. The consistent improvements indicate the effectiveness, robustness, and universality of RealHNS, which is also easy-to-deploy in real-world systems as a plug-and-play strategy. The source code is avaliable in https://github.com/hulkima/RealHNS.

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Cited By

Wang YGe XChen XXie RYan SZhang XChen ZMa JXin XNejdl WAuer SKarras OCha MMoens MNajork M(2025)Exploration and Exploitation of Hard Negative Samples for Cross-Domain Sequential RecommendationProceedings of the Eighteenth ACM International Conference on Web Search and Data Mining10.1145/3701551.3703535(669-677)Online publication date: 10-Mar-2025
https://dl.acm.org/doi/10.1145/3701551.3703535
Acharyya SPervin N(2025)Enhancing cross-domain recommendations: Leveraging personality-based transfer learning with probabilistic matrix factorizationExpert Systems with Applications10.1016/j.eswa.2024.125667263(125667)Online publication date: Mar-2025
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Ma HXie RMeng LYang YSun XKang ZLarson K(2024)SeeDRecProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/251(2270-2278)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/251
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Index Terms

Exploring False Hard Negative Sample in Cross-Domain Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems

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cover image ACM Conferences

RecSys '23: Proceedings of the 17th ACM Conference on Recommender Systems

September 2023

1406 pages

ISBN:9798400702419

DOI:10.1145/3604915

Copyright © 2023 ACM.

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Published: 14 September 2023

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Excellent Youth Scholars Program of Shandong Province
TaiShan Scholars Program
National Natural Science Foundation of China

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RecSys '23

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RecSys '23: Seventeenth ACM Conference on Recommender Systems

September 18 - 22, 2023

Singapore, Singapore

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

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Cited By

Wang YGe XChen XXie RYan SZhang XChen ZMa JXin XNejdl WAuer SKarras OCha MMoens MNajork M(2025)Exploration and Exploitation of Hard Negative Samples for Cross-Domain Sequential RecommendationProceedings of the Eighteenth ACM International Conference on Web Search and Data Mining10.1145/3701551.3703535(669-677)Online publication date: 10-Mar-2025
https://dl.acm.org/doi/10.1145/3701551.3703535
Acharyya SPervin N(2025)Enhancing cross-domain recommendations: Leveraging personality-based transfer learning with probabilistic matrix factorizationExpert Systems with Applications10.1016/j.eswa.2024.125667263(125667)Online publication date: Mar-2025
https://doi.org/10.1016/j.eswa.2024.125667
Ma HXie RMeng LYang YSun XKang ZLarson K(2024)SeeDRecProceedings of the Thirty-Third International Joint Conference on Artificial Intelligence10.24963/ijcai.2024/251(2270-2278)Online publication date: 3-Aug-2024
https://dl.acm.org/doi/10.24963/ijcai.2024/251
Mezentsev GGusak DOseledets IFrolov E(2024)Scalable Cross-Entropy Loss for Sequential Recommendations with Large Item CatalogsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688140(475-485)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688140
Vente TWegmeth LSaid ABeel J(2024)From Clicks to Carbon: The Environmental Toll of Recommender SystemsProceedings of the 18th ACM Conference on Recommender Systems10.1145/3640457.3688074(580-590)Online publication date: 8-Oct-2024
https://dl.acm.org/doi/10.1145/3640457.3688074
Ma HXie RMeng LChen XZhang XLin LZhou J(2024)Triple Sequence Learning for Cross-domain RecommendationACM Transactions on Information Systems10.1145/363835142:4(1-29)Online publication date: 9-Feb-2024
https://dl.acm.org/doi/10.1145/3638351
Wang XXiao XZhang RZhang XNa TTenneti TWang HMa FBaeza-Yates RBonchi F(2024)Mitigating Pooling Bias in E-commerce Search via False Negative EstimationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671630(5917-5925)Online publication date: 25-Aug-2024
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Wang RQi ZMeng XMeng L(2023)Learning to Fuse Residual and Conditional Information for Video Compression and ReconstructionImage and Graphics 10.1007/978-3-031-46314-3_29(360-372)Online publication date: 22-Sep-2023
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Li JMa HLi XQi ZMeng XMeng L(2023)Unsupervised Segmentation of Haze Regions as Hard Attention for Haze ClassificationImage and Graphics 10.1007/978-3-031-46314-3_28(346-359)Online publication date: 22-Sep-2023
https://dl.acm.org/doi/10.1007/978-3-031-46314-3_28

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