提出轻量框架LAFB，缓解视频推荐中熟悉内容过度曝光问题。
Learning to Alleviate Familiarity Bias in Video Recommendation
- 基于用户互动特征建模内容熟悉度，动态调整评分
- 显著提升新内容观看时长与创作者曝光，保持整体满意度
- 已部署于YouTube，适合关注公平推荐的平台方
现代视频推荐系统虽以提升用户参与度为目标，却常因行为偏差导致内容曝光失衡。本文聚焦排序后阶段，提出LAFB（Learning to Alleviate Familiarity Bias）框架，一种轻量且模型无关的去偏方法。LAFB通过离散与连续的交互特征建模用户-内容熟悉度，估计个性化去偏因子，调整用户评分预测，降低熟悉内容在最终排序中的主导地位。我们在真实推荐系统中开展大规模离线评估与在线A/B测试，统一部署栈下对比了可部署的流行度导向方案。结果表明，LAFB提升了新内容观看时长占比，改善了新兴创作者和整体内容多样性曝光，同时维持稳定总体观看时长与短期满意度。LAFB已上线YouTube的排序后阶段，验证了其在实际应用中的有效性。
原文摘要 · Abstract (English)
Modern video recommendation systems aim to optimize user engagement and platform objectives, yet often face structural exposure imbalances caused by behavioral biases. In this work, we focus on the post-ranking stage and present LAFB (Learning to Alleviate Familiarity Bias), a lightweight and model-agnostic framework designed to mitigate familiarity bias in recommendation outputs. LAFB models user-content familiarity using discrete and continuous interaction features, and estimates personalized debiasing factors to adjust user rating prediction scores, thereby reducing the dominance of familiar content in the final ranking. We conduct large-scale offline evaluations and online A/B testing in a real-world recommendation system, under a unified serving stack that also compares LAFB with deployable popularity-oriented remedies. Results show that LAFB increases novel watch-time share and improves exposure for emerging creators and overall content diversity, while maintaining stable overall watch time and short-term satisfaction. LAFB has already been launched in the post-ranking stage of YouTube's recommendation system, demonstrating its effectiveness in real-world applications.
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