Mend the Measurement Gap: Latent User Preference Modeling for Short-Form Video Recommendation
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 improv...
| Authors | Shuo Chang, Yueqi Wang, Zihuan Diao et al. |
|---|---|
| Primary category | cs.IR |
| Published | 26 September 2026 |
| Last updated at source | 26 September 2026 |
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