Content Depth Matters in Short-Video Recommendation: Rethinking the Attention Economy

• URL: http://arxiv.org/abs/2608.13990v1
• Date: Fri, 14 Aug 2026 06:13:40 GMT
• Title: Content Depth Matters in Short-Video Recommendation: Rethinking the Attention Economy
• Abstract summary: This paper introduces a new metric, the textbfContent Depth Score (CDS), to quantify the content depth of short videos.<n> CDS measures the extent to which a video is expected to stimulate higher-order cognitive processes.<n>We present textbfSCOPE-Bench, the first benchmark for content-depth evaluation in short-video recommendation.
• Score: 34.25398134800663
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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