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Negative Sampling in Recommendation: A Survey and Future Directions

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arxiv 2409.07237 v2 pith:EHREKXSU submitted 2024-09-11 cs.IR

Haokai Ma , Ruobing Xie , Lei Meng , Fuli Feng , Xiaoyu Du , Xingwu Sun , Zhanhui Kang , Xiangxu Meng This is my paper · ORCID
classification cs.IR
keywords negativesamplinguserbehaviorsfeedbackdirectionsexistinginformation
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Recommender system (RS) aims to capture personalized preferences from massive user behaviors, making them pivotal in the era of information explosion. However, the presence of ``information cocoons'', interaction sparsity, cold-start problem and feedback loops inherent in RS make users interact with a limited number of items. Conventional recommendation algorithms typically focus on the positive historical behaviors, while neglecting the essential role of negative feedback in user preference understanding. As a promising but easy-to-ignored area, negative sampling is proficients in revealing the genuine negative aspect inherent in user behaviors, emerging as an inescapable procedure in RS. In this survey, we first discuss existing user feedback, the critical role of negative sampling and the optimization objectives in RS and thoroughly analyze challenges that consistently impede its progress. Then, we conduct an extensive literature review on the existing negative sampling strategies in RS and classify them into five categories with their discrepant techniques. Finally, we detail the insights of the tailored negative sampling strategies in diverse RS scenarios and outline an overview of the prospective research directions toward which the community may engage and benefit.

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Cited by 4 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. GFlowGR: Fine-tuning Generative Recommendation Frameworks with Generative Flow Networks

    cs.IR 2025-06 conditional novelty 6.0 of 10

    GFlowGR fine-tunes generative recommender LLMs with GFlowNet losses and multi-signal rewards, beating SFT, DPO, and GRPO baselines on three datasets and in production.

  2. FORGE: Forming Semantic Identifiers for Generative Retrieval in Industrial Datasets

    cs.IR 2025-09 conditional novelty 5.0 of 10

    FORGE shows that balancing codebook usage and adding multimodal side information improves semantic identifiers for generative retrieval, validated offline and on Taobao.

  3. USD: A User-Intent-Driven Sampling and Dual-Debiasing Framework for Large-Scale Homepage Recommendations

    cs.IR 2025-07 reject novelty 5.0 of 10

    USD combines portal-intent-based sampling with dual intent-weighted debiasing to improve homepage CTR prediction, reporting 35.4% and 14.5% UCTR gains in online tests.

  4. Listwise Preference Alignment Optimization for Tail Item Recommendation

    cs.IR 2025-07 reject novelty 4.0 of 10

    The paper applies a listwise softmax preference loss with head-item negative sampling and tail reweighting to improve long-tail item recommendation.

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