Users derive value from a recommender system (RS) only to the extent that it is able to surface content (or items) that meet their needs/preferences. While RSs often have a comprehensive view of user preferences across the entire user base, content providers, by contrast, generally have only a local view of the preferences of users that have interacted with their content. This limits a provider's ability to offer new content to best serve the broader population. In this work, we tackle this information asymmetry with content prompting policies. A content prompt is a hint or suggestion to a provider to make available novel content for which the RS predicts unmet user demand. A prompting policy is a sequence of such prompts that is responsive to the dynamics of a provider's beliefs, skills and incentives. We aim to determine a joint prompting policy that induces a set of providers to make content available that optimizes user social welfare in equilibrium, while respecting the incentives of the providers themselves. Our contributions include: (i) an abstract model of the RS ecosystem, including content provider behaviors, that supports such prompting; (ii) the design and theoretical analysis of sequential prompting policies for individual providers; (iii) a mixed integer programming formulation for optimal joint prompting using path planning in content space; and (iv) simple, proof-of-concept experiments illustrating how such policies improve ecosystem health and user welfare.

翻译：用户从推荐系统（RS）中获取价值的程度，取决于该系统能否高效呈现符合其需求/偏好的内容（或物品）。虽然推荐系统通常能全面了解整个用户群体的偏好，但相比之下，内容提供者一般只能掌握与其内容互动的用户的局部偏好。这限制了提供者提供新内容以更好地服务更广泛用户的能力。在本研究中，我们通过内容提示策略应对这一信息不对称问题。内容提示是一种向提供者提出的建议或提示，旨在促使提供者提供推荐系统预测存在未满足用户需求的新内容。提示策略则是一系列此类提示的序列，能够响应提供者信念、技能和激励的动态变化。我们旨在确定一种联合提示策略，该策略能促使一组提供者提供内容，在均衡状态下优化用户社会福利，同时尊重提供者自身的激励机制。我们的贡献包括：(i) 构建支持此类提示的推荐系统生态抽象模型，涵盖内容提供者行为；(ii) 针对单个提供者的顺序提示策略设计及理论分析；(iii) 基于内容空间路径规划的混合整数规划形式化方法，用于实现最优联合提示；(iv) 简单的概念验证实验，展示此类策略如何改善生态系统健康与用户福利。