表演性推荐：通过战略激励实现内容多样化
Performative Recommendation: Diversifying Content via Strategic Incentives
AI总结：
针对推荐系统因追求准确性导致多样性不足的问题，提出利用推荐的表演性特性，通过可预判内容战略变化、惩罚同质性的新型正则化方法激励创作者产出多样内容，经分析与实验验证了方法的有效性。
摘要：
推荐系统的主要目标是向用户推荐相关内容，但针对准确性优化往往会导致推荐结果缺乏多样性。为解决这一问题，重排序等传统方法通过展示更多样化的物品来提升多样性。本文认为，要促进内在且持久的多样性，系统必须鼓励多样性内容的创作。为此，我们利用推荐的表演性特性，展示了学习如何能够激励战略型内容创作者创作多样化内容。我们的方法基于一种新型正则化方式，该方式可预判内容的战略性变化，并对内容同质性进行惩罚。我们提供了分析与实证结果，阐明了何时以及如何能够激励多样性，并通过实验在合成数据与半合成数据上验证了我们方法的有效性。
英文摘要：
The primary goal in recommendation is to suggest relevant content to users, but optimizing for accuracy often results in recommendations that lack diversity. To remedy this, conventional approaches such as re-ranking improve diversity by presenting more diverse items. Here we argue that to promote inherent and prolonged diversity, the system must encourage its creation. Towards this, we harness the performative nature of recommendation, and show how learning can incentivize strategic content creators to create diverse content. Our approach relies on a novel form of regularization that anticipates strategic changes to content, and penalizes for content homogeneity. We provide analytic and empirical results that demonstrate when and how diversity can be incentivized, and experimentally demonstrate the utility of our approach on synthetic and semi-synthetic data.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
