Mend the Measurement Gap: Latent User Preference Modeling for Short-Form Video Recommendation

Shuo Chang; Yueqi Wang; Zihuan Diao; Ali Montazer; Jiangguo Zhang; Joyneel Misra; Dapeng Hong; Tomer Margolin; Sourabh Bansod; Ningren Han

doi:10.1145/3773078.3831923

arXiv · 2609.32839

Mend the Measurement Gap: Latent User Preference Modeling for Short-Form Video Recommendation

Shuo Chang·Yueqi Wang·Zihuan Diao·Ali Montazer·Jiangguo Zhang·Joyneel Misra·Dapeng Hong·Tomer Margolin·Sourabh Bansod·Ningren Han

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Abstract

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.

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Shuo Chang, Yueqi Wang, Zihuan Diao, Ali Montazer, Jiangguo Zhang, Joyneel Misra, Dapeng Hong, Tomer Margolin, Sourabh Bansod, Ningren Han. 2026-09-26. Mend the Measurement Gap: Latent User Preference Modeling for Short-Form Video Recommendation. https://doi.org/10.1145/3773078.3831923

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