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

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.

Information Retrieval (cs.IR); Machine Learning (cs.LG)

arXiv:2609.32839 [cs.IR]

(or arXiv:2609.32839v1 [cs.IR] for this version)

https://doi.org/10.48550/arXiv.2609.32839 Focus to learn more arXiv-issued DOI via DataCite (pending registration)

In 20th ACM Conference on Recommender Systems (RecSys '26), September 27-October 02, 2026, Minneapolis, MN, USA. ACM, 7 pages

https://doi.org/10.1145/3773078.3831923 Focus to learn more DOI(s) linking to related resources

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