解决本地社区平台的无效负样本问题，提升用户建模精度
Don't Contrast the Impossible: Region-Constrained Batching for Contrastive User Modeling on a Local Community Platform
- 按地理区域构建同质批次，只对比用户可能看到的物品
- 线上测试显示推荐与广告排序效果显著提升
- 适合本地化推荐系统开发者参考使用
对比学习广泛用于大规模推荐系统中的用户建模，标准的批内负样本假设所有用户都能接触任意物品。但在像Karrot这样的本地社区平台中，曝光具有地理限制；许多用户-物品对本就不可能发生，却仍被当作负样本训练，削弱了对比学习信号。我们提出区域约束批采样（RCBS），一种简单有效的批处理方法，通过构造区域同质的迷你批次，使用户主要与实际可接触的物品进行对比。通过用可行负样本替代不可能负样本，RCBS在真实曝光条件下自然引入更难且信息量更大的负样本。离线评估和线上A/B测试表明，RCBS持续提升了用户表征质量，进而改善首页推荐、检索和展示广告排序。最终生成的用户嵌入已部署至生产环境，应用于多种场景。
原文摘要 · Abstract (English)
Contrastive learning is widely used for user modeling in large-scale recommender systems, where standard in-batch negatives implicitly assume universal exposure that any user can be shown any item. On local community platforms such as Karrot, however, exposure is geographically constrained; many user-item pairs are impossible by design yet still treated as negatives during training, diluting the contrastive learning signal. We address this impossible negatives problem and propose Region-Constrained Batch Sampling (RCBS), a simple yet effective batching method that constructs region-homogeneous mini-batches so that users are contrasted primarily against items they could feasibly see. By replacing impossible negatives with feasible ones, RCBS naturally introduces harder and more informative negatives under realistic exposure constraints. With offline evaluations and online A/B tests, we show that RCBS consistently improves user representation quality and consequently enhances home feed ranking, retrieval, and display ads ranking. The resulting user embeddings have been deployed in production across various applications.
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