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：现代推荐系统位于复杂生态系统的核心，这些系统耦合了用户、内容提供者、广告商及其他参与者的行为。然而，大多数推荐研究（以及几乎所有具有重要影响力的实际推荐系统）的重点仍局限于对单个用户进行局部、短视的推荐优化。这严重影响了推荐系统本可为用户带来的长期效用。我们认为，若要最大化系统为各参与者创造的价值并改善整体生态系统"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关系。这要求：利用强化学习等技术进行长周期优化；运用社会选择方法在不同参与者的效用分配中做出不可避免的权衡；借助机制设计工具减少信息不对称，同时考虑激励与策略性行为；结合行为经济学与心理学概念优化用户与内容提供者行为建模；利用生成模型与基础模型的最新进展使上述机制具有可解释性和可操作性。我们提出一个整合上述要素的概念框架，并阐述这些不同学科交叉领域涌现出的系列研究挑战。