Modern recommender systems lie at the heart of complex ecosystems that couple the behavior of users, content providers, advertisers, and other actors. Despite this, the focus of the majority of recommender research -- and most practical recommenders of any import -- is on the local, myopic optimization of the recommendations made to individual users. This comes at a significant cost to the long-term utility that recommenders could generate for its users. We argue that explicitly modeling the incentives and behaviors of all actors in the system -- and the interactions among them induced by the recommender's policy -- is strictly necessary if one is to maximize the value the system brings to these actors and improve overall ecosystem "health". Doing so requires: optimization over long horizons using techniques such as reinforcement learning; making inevitable tradeoffs in the utility that can be generated for different actors using the methods of social choice; reducing information asymmetry, while accounting for incentives and strategic behavior, using the tools of mechanism design; better modeling of both user and item-provider behaviors by incorporating notions from behavioral economics and psychology; and exploiting recent advances in generative and foundation models to make these mechanisms interpretable and actionable. We propose a conceptual framework that encompasses these elements, and articulate a number of research challenges that emerge at the intersection of these different disciplines.

翻译：现代推荐系统处于复杂生态系统的核心，这些系统耦合了用户、内容提供商、广告商及其他参与者的行为。然而，多数推荐研究（以及几乎所有具有实际影响力的推荐系统）仍侧重于对个体用户推荐行为的局部、短视优化——这严重损害了推荐系统本可为用户创造的长期效用。我们认为，若要将系统为这些参与者创造的价值最大化并提升整体生态系统"健康度"，必须显式建模系统中所有参与者的激励与行为，以及推荐策略引发的参与者间交互。实现这一目标需要：采用强化学习等技术进行长期优化；运用社会选择方法在面向不同参与者的效用之间做出不可避免的权衡；借助机制设计工具降低信息不对称性，同时考虑激励因素与策略性行为；通过引入行为经济学与心理学概念改进用户与内容提供方行为建模；利用生成式模型与基础模型的最新进展使这些机制具备可解释性与可操作性。我们提出了一个整合上述要素的概念框架，并阐明了这些学科交叉领域涌现的多项研究挑战。