Most existing recommender systems focus primarily on matching users to content which maximizes user satisfaction on the platform. It is increasingly obvious, however, that content providers have a critical influence on user satisfaction through content creation, largely determining the content pool available for recommendation. A natural question thus arises: can we design recommenders taking into account the long-term utility of both users and content providers? By doing so, we hope to sustain more providers and a more diverse content pool for long-term user satisfaction. Understanding the full impact of recommendations on both user and provider groups is challenging. This paper aims to serve as a research investigation of one approach toward building a provider-aware recommender, and evaluating its impact in a simulated setup. To characterize the user-recommender-provider interdependence, we complement user modeling by formalizing provider dynamics as well. The resulting joint dynamical system gives rise to a weakly-coupled partially observable Markov decision process driven by recommender actions and user feedback to providers. We then build a REINFORCE recommender agent, coined EcoAgent, to optimize a joint objective of user utility and the counterfactual utility lift of the provider associated with the recommended content, which we show to be equivalent to maximizing overall user utility and the utilities of all providers on the platform under some mild assumptions. To evaluate our approach, we introduce a simulation environment capturing the key interactions among users, providers, and the recommender. We offer a number of simulated experiments that shed light on both the benefits and the limitations of our approach. These results help understand how and when a provider-aware recommender agent is of benefit in building multi-stakeholder recommender systems.

中文翻译：

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面向内容提供商感知推荐系统：用户与提供商实用程序之间相互作用的模拟研究

大多数现有的推荐系统主要致力于将用户与内容匹配，以最大程度地提高平台上的用户满意度。但是，越来越明显的是，内容提供商通过内容创建对用户满意度产生关键影响，很大程度上决定了可用于推荐的内容池。由此产生一个自然的问题：我们是否可以在考虑用户和内容提供商的长期效用的情况下设计推荐器？这样，我们希望维持更多的提供商和更多的内容库，以使用户长期满意。理解建议对用户和提供者群体的全面影响具有挑战性。本文旨在作为研究一种方法的研究调查，该方法用于构建提供者感知的推荐器，并评估其在模拟设置中的影响。为了描述用户推荐者与提供者之间的相互依赖性，我们还通过规范提供者动态来补充用户建模。由此产生的联合动力系统产生了一个弱耦合的，部分可观察的马尔可夫决策过程，该过程由推荐者的动作和提供者的用户反馈驱动。然后，我们建立了一个REINFORCE推荐代理，即EcoAgent，以优化用户效用和与推荐内容相关的提供商的反事实效用提升的共同目标，我们证明这相当于最大化整体用户效用和所有提供商的效用在某些温和假设下的平台上。为了评估我们的方法，我们引入了一个模拟环境，该环境捕获了用户，提供者和推荐者之间的关键交互。我们提供了许多模拟实验，阐明了我们方法的优点和局限性。这些结果有助于了解提供者感知的推荐人代理在构建多利益相关者推荐人系统时如何以及何时受益。