AI 中文总结
本文针对短视频推荐系统过度偏好浅层内容的问题，提出内容深度评分（CDS）指标与SCOPE-Bench基准，评估13种推荐系统后发现其偏好浅层内容，相关算法表现仅略优于随机选择，揭示了现有推荐目标的局限性。
AI 中文摘要
在注意力经济的驱动下，短视频推荐系统（RS）的优化目标主要是通过推广能在几秒内吸引用户注意力的视频，以最大化用户参与度。这些系统天生倾向于浅层内容视频，这类视频能有效吸引即时注意力。然而，越来越多证据表明，长期接触此类内容可能会对用户的认知参与度和心理健康产生负面影响，引发了人们对短视频平台长期社会影响的担忧。为应对这一挑战，本文提出了一个新的指标——内容深度评分（Content Depth Score, CDS），用于量化短视频的内容深度。CDS基于成熟的认知心理学和学习理论，采用七级量表，衡量视频预期激发高阶认知过程的程度。作为实现这一愿景的初步步骤，我们推出了首个用于短视频推荐内容深度评估的基准——SCOPE-Bench。该基准基于大规模开源短视频数据集构建，为15万条视频提供CDS标注，支持从认知内容视角对推荐系统进行系统评估。借助SCOPE-Bench，我们评估了13个代表性推荐系统，发现它们一致偏好浅层内容视频。此外，我们还发现，这些推荐认知深度内容的算法仅比随机选择略好，凸显了现有推荐目标此前被忽视的局限性。我们的代码和数据集可在该https网址获取。
英文摘要
Driven by the attention economy, short-video Recommender Systems (RSs) are primarily optimized to maximize user engagement by promoting videos that capture attention within seconds. These systems inherently favor shallow-content videos that are effective at attracting immediate attention. However, growing evidence suggests that prolonged exposure to such content may negatively affect users' cognitive engagement and mental well-being, raising concerns about the long-term societal impact of the short-video platform. To tackle this challenge, this paper introduces a new metric, the \textbf{Content Depth Score (CDS)}, to quantify the content depth of short videos. CDS measures the extent to which a video is expected to stimulate higher-order cognitive processes, using a seven-level scale grounded in established theories of cognitive psychology and learning. As an initial step toward this vision, we present \textbf{SCOPE-Bench}, the first benchmark for content-depth evaluation in short-video recommendation. Built upon a large-scale open-source short-video dataset, SCOPE-Bench provides CDS annotations for 150K videos, enabling systematic evaluation of RSs from a cognitive-content perspective. Leveraging SCOPE-Bench, we evaluate 13 representative RSs and reveal a consistent preference for shallow-content videos. Moreover, we find that these algorithms recommending cognitively deep content are only marginally better than random selection, highlighting a previously overlooked limitation of existing recommendation objectives. Our code and datasets are available at https://liweidengdavid.github.io/SCOPE-Bench/.
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