弥合测量差距：面向短视频推荐的潜在用户偏好建模
Mend the Measurement Gap: Latent User Preference Modeling for Short-Form Video Recommendation
- Google（谷歌）
- YouTube（优兔）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
针对短视频推荐中行为反馈受时长等混杂因素干扰的问题，提出因子化潜在价值模型（FLVM），从异构反馈中分离测量噪声并估计偏好价值，在YouTube Shorts上提升离线指标及在线观众享受指标2.67%。
AI中文摘要：
推荐系统严重依赖异构行为反馈来推断用户偏好。尽管这些信号丰富，但它们是不完美的测量：相同的观察行为可能源于不同的潜在状态，例如真正的享受、被动消费或注意力不集中。这一挑战在短视频中尤为严峻，因为基于观看的信号受到视频时长等测量混杂因素的强烈影响——相同的观看时间对不同长度的视频可能意味着不同的偏好水平，而基于比率的指标可能系统性地偏向短视频。因此，优化原始参与度可能会放大测量伪影，而不是提升用户价值。我们提出了一种因子化潜在价值模型（FLVM），用于从异构行为反馈中测量用户偏好。该模型将观察到的行为视为低维因子化潜在价值状态的噪声测量，并使用结构化输出头来建模异构反馈信号。一条受限基线路径捕获来自测量混杂特征（如视频时长、用户倾向和会话上下文）的可预测变化，而一条路由潜在路径估计与偏好相关的价值优势。由此产生的潜在价值评分可以作为排序特征或排序分数集成到现有推荐系统中。在YouTube Shorts（一个主要的短视频平台）上，该模型改善了离线指标，并在在线A/B测试中将主要观众享受指标提升了2.67%。
英文摘要：
Recommender systems rely heavily on heterogeneous behavioral feedback to infer user preference. Although abundant, these signals are imperfect measurements: the same observed behavior can arise from different underlying states, such as genuine enjoyment, passive consumption, or inattention. The challenge is especially acute in short-form video, where watch-based signals are strongly affected by measurement confounders such as video duration - the same watch time can imply different levels of preference for videos of different lengths, while ratio-based metrics can systematically favor short videos. As a result, optimizing raw engagement can amplify measurement artifacts rather than improving user value. We propose a Factorized Latent Value Model (FLVM) for measuring user preference from heterogeneous behavioral feedback. The model treats observed behaviors as noisy measurements of a low-dimensional, factorized latent value state and uses structured output heads to model heterogeneous feedback signals. A restricted baseline path captures predictable variation from measurement-confounding features such as video duration, user propensity, and session context, while a routed latent path estimates preference-relevant value advantage. The resulting latent value score can be integrated into an existing recommender system as a ranking feature or ranking score. On YouTube Shorts, a major short-form video platform, this model improves offline metrics and lifts a primary viewer enjoyment metric by 2.67% in online A/B tests.
