AI 中文总结
针对工业推荐系统偏差问题，提出ConAlign条件去偏方法，通过离散门控条件对齐机制平衡事实准确性和无偏偏好估计，支持实时流适应，经实验验证有效，提升了快手用户长期参与度和兴趣多样性。
AI 中文摘要
基于观测数据训练的行业推荐系统存在各种偏差，导致用户兴趣单一且长期参与度下降。利用无偏数据去偏虽有前景，但现有方法因忽视事实（有偏）推荐性能和计算开销大等局限，不适用于工业部署。为此提出ConAlign，一种用于工业部署的条件去偏方法。其关键创新在于基于离散门控的条件对齐机制，能在支持实时流适应时平衡事实准确性和无偏偏好估计。它是首个成功部署在大规模工业推荐系统的流去偏推荐框架，利用少量无偏随机流量去偏。在三个真实数据集上的离线实验验证了框架有效性，快手的大规模在线A/B测试表明其显著提升了长期用户参与度和兴趣多样性，且延迟开销可忽略不计。
英文摘要
Industry recommender systems trained on observational data suffer from various biases that create filter bubbles, causing user interests to collapse into narrow categories and severely degrading long-term engagement. While utilizing unbiased uniform data for debiasing has shown promise, existing methods remain impractical for industrial deployment due to limitations such as neglect of factual (biased) recommendation performance and the substantial computational overhead. To overcome these limitations, we propose ConAlign (Conditional Alignment Framework), a conditional debiasing approach for industrial deployment. The key innovation of ConAlign lies in a discrete gating-based conditional alignment mechanism that selectively transfers knowledge from the biased tower to the unbiased tower. Following a selective intervention paradigm rather than universal correction, it seamlessly balances factual accuracy and unbiased preference estimation while supporting real-time streaming adaptation. To the best of our knowledge, ConAlign is the first streaming debiasing recommendation framework successfully deployed in a large-scale industrial recommendation system that utilizes a small fraction of unbiased random traffic for debiasing. Extensive offline experiments on three real-world datasets rigorously validate the effectiveness of our proposed framework. Furthermore, large-scale online A/B testing on Kuaishou demonstrates significant improvements in long-term user engagement and interest diversity, with negligible latency overhead.
