通过细粒度分析视频跳过时机，提升短视频推荐效果。
Exploiting Fine-Grained Skip Behaviors for Micro-Video Recommendation
- 按跳过时间分三类：立即跳过为负，延迟跳过为弱正，无跳过为强正。
- 在两个数据集上，8项指标均优于传统方法。
- 适合关注短视频用户行为建模的研究者。
社交平台短视频分享日益普及，推动了微视频推荐研究的发展。然而，传统方法将用户交互简化为跳过或未跳过两类，忽略了关键信息。本文关注视频前几秒的重要性，将跳过行为细分为三类：短时间内跳过为负面，延迟跳过为弱正面，无跳过为强正面。提出双层图结构与分层排序损失，有效建模这些细粒度信号。实验表明，该方法在两个公开数据集上，八项评估指标均优于三种基准方法。
原文摘要 · Abstract (English)
The growing trend of sharing short videos on social media platforms, where users capture and share moments from their daily lives, has led to an increase in research efforts focused on micro-video recommendations. However, conventional methods oversimplify the modeling of skip behavior, categorizing interactions solely as positive or negative based on whether skipping occurs. This study was motivated by the importance of the first few seconds of micro-videos, leading to a refinement of signals into three distinct categories: highly positive, less positive, and negative. Specifically, we classify skip interactions occurring within a short time as negatives, while those occurring after a delay are categorized as less positive. The proposed dual-level graph and hierarchical ranking loss are designed to effectively learn these fine-grained interactions. Our experiments demonstrated that the proposed method outperformed three conventional methods across eight evaluation measures on two public datasets.
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