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

arXiv: https://arxiv.org/abs/2608.13990 · HTML: https://mlsanity.com/paper/2608.13990/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 24 of its 57 references that are in the map.
- First posted on arXiv 14 Aug 2026.

## Builds on (most cited first, 24 of 24)

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- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
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- [Prometheus: Inducing Fine-grained Evaluation Capability in Language Models](https://mlsanity.com/paper/2310.08491/) (2023)
- [PandaLM: An Automatic Evaluation Benchmark for LLM Instruction Tuning Optimization](https://mlsanity.com/paper/2306.05087/) (2023)
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- [VBPR: Visual Bayesian Personalized Ranking from Implicit Feedback](https://mlsanity.com/paper/1510.01784/) (2015)
- [JFLEG: A Fluency Corpus and Benchmark for Grammatical Error Correction](https://mlsanity.com/paper/1702.04066/) (2017)
- [Mining Latent Structures for Multimedia Recommendation](https://mlsanity.com/paper/2104.09036/) (2021)
- [GRCN: Graph-Refined Convolutional Network for Multimedia Recommendation with Implicit Feedback](https://mlsanity.com/paper/2111.02036/) (2021)
- [Video-Bench: Human-Aligned Video Generation Benchmark](https://mlsanity.com/paper/2504.04907/) (2025)
- [Bootstrap Latent Representations for Multi-modal Recommendation](https://mlsanity.com/paper/2207.05969/) (2022)
- [A Tale of Two Graphs: Freezing and Denoising Graph Structures for Multimodal Recommendation](https://mlsanity.com/paper/2211.06924/) (2022)
- [LGMRec: Local and Global Graph Learning for Multimodal Recommendation](https://mlsanity.com/paper/2312.16400/) (2023)
- [Multi-View Graph Convolutional Network for Multimedia Recommendation](https://mlsanity.com/paper/2308.03588/) (2023)
- [DiffMM: Multi-Modal Diffusion Model for Recommendation](https://mlsanity.com/paper/2406.11781/) (2024)
- [A Large-scale Dataset with Behavior, Attributes, and Content of Mobile Short-video Platform](https://mlsanity.com/paper/2502.05922/) (2025)
- [A Survey of Real-World Recommender Systems: Challenges, Constraints, and Industrial Perspectives](https://mlsanity.com/paper/2509.06002/) (2025)
- [FITMM: Adaptive Frequency-Aware Multimodal Recommendation via Information-Theoretic Representation Learning](https://mlsanity.com/paper/2601.22498/) (2026)
- [Refining Contrastive Learning and Homography Relations for Multi-Modal Recommendation](https://mlsanity.com/paper/2508.13745/) (2025)

## Abstract (arXiv)

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 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 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/.