为大规模双塔检索模型（常用于推荐系统）开发了一种新的自监督硬负例采样技术。该方法利用大型语言模型（LLM）在训练期间实时聚类并生成具有挑战性的负例。该方法旨在通过提供比传统批内或批外方法更有信息量的负例来提高模型性能。实验和大规模在线系统的部署表明，该技术超越了当前行业标准，有助于缓解反馈循环，并减少了流行度偏差。 AI
影响 通过提高训练数据质量和减少偏差来增强推荐系统性能。
排序理由 详细介绍AI模型训练新技术的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →
完整方法见我们的编辑标准。
arXiv:2607.00448v1 Announce Type: cross Abstract: The two-tower model has been widely used for large-scale recommendation systems, particularly in the retrieval stage. Industry standards for training two-tower models typically involve in-batch and/or out-of-batch negative samplin…
The two-tower model has been widely used for large-scale recommendation systems, particularly in the retrieval stage. Industry standards for training two-tower models typically involve in-batch and/or out-of-batch negative sampling. However, these methods often produce easy negat…