Recommender ecosystems are an emerging subject of research. Such research examines how the characteristics of algorithms, recommendation consumers, and item providers influence system dynamics and long-term outcomes. One architectural possibility that has not yet been widely explored in this line of research is the consequences of a configuration in which recommendation algorithms are decoupled from the platforms they serve. This is sometimes called "the friendly neighborhood algorithm store" or "middleware" model. We are particularly interested in how such architectures might offer a range of different distributions of utility across consumers, providers, and recommendation platforms. In this paper, we create a model of a recommendation ecosystem that incorporates algorithm choice and examine the outcomes of such a design.

翻译：推荐生态系统是一个新兴的研究课题。此类研究探讨算法特性、推荐消费者和项目提供者如何影响系统动态与长期结果。在该研究领域中，一个尚未被广泛探索的架构可能性是：当推荐算法与其服务平台解耦时会产生何种后果。这种架构有时被称为“友好邻域算法商店”或“中间件”模型。我们特别关注此类架构如何为消费者、提供者和推荐平台之间提供多样化的效用分配方案。本文构建了一个包含算法选择的推荐生态系统模型，并对此类设计产生的结果进行了分析。