Short-video applications have attracted substantial user traffic. However, these platforms also foster problematic usage patterns, commonly referred to as short-video addiction, which pose risks to both user health and the sustainable development of platforms. Prior studies on this issue have primarily relied on questionnaires or volunteer-based data collection, which are often limited by small sample sizes and population biases. In contrast, short-video platforms have large-scale behavioral data, offering a valuable foundation for analyzing addictive behaviors. To examine addiction-aware behavior patterns, we combine economic addiction theory with users' implicit behavior captured by recommendation systems. Our analysis shows that short-video addiction follows functional patterns similar to traditional forms of addictive behavior (e.g., substance abuse) and that its intensity is consistent with findings from previous social science studies. To develop a simulator that can learn and model these patterns, we introduce a novel training framework, AddictSim. To consider the personalized addiction patterns, AddictSim uses a mean-to-adapted strategy with group relative policy optimization training. Experiments on two large-scale datasets show that AddictSim consistently outperforms existing training strategies. Our simulation results show that integrating diversity-aware algorithms can mitigate addictive behaviors well.

翻译：短视频应用已吸引了大量用户流量。然而，这些平台也催生了被称为“短视频成瘾”的问题性使用模式，对用户健康及平台的可持续发展均构成风险。以往针对此问题的研究主要依赖问卷调查或志愿者数据收集，这些方法常受限于样本量小和人群偏差。相比之下，短视频平台拥有大规模行为数据，为分析成瘾行为提供了宝贵基础。为探究具有成瘾意识的行为模式，我们将经济成瘾理论与推荐系统捕获的用户隐式行为相结合。分析表明，短视频成瘾遵循与传统成瘾行为（如物质滥用）相似的功能模式，其强度与先前社会科学研究的发现一致。为开发能够学习并建模这些模式的模拟器，我们提出了一种新型训练框架AddictSim。为考虑个性化成瘾模式，AddictSim采用均值自适应策略与群体相对策略优化训练。在两个大规模数据集上的实验表明，AddictSim持续优于现有训练策略。我们的模拟结果显示，整合多样性感知算法能有效缓解成瘾行为。