探索推荐算法与平台解耦的新架构，提升各方长期收益分配
Decoupled Recommender Systems: Exploring Alternative Recommender Ecosystem Designs
- 将推荐算法独立于平台，形成可替换的中间件模式
- 不同用户和商家的收益分布随算法选择产生显著差异
- 适合关注公平性与生态可持续性的推荐系统研究者
推荐生态系统是新兴的研究领域，关注算法特性、消费者与内容提供方如何共同影响系统动态与长期结果。本文探讨一种尚未广泛研究的架构：推荐算法与所服务的平台解耦，即所谓‘友好邻居算法商店’或‘中间件’模型。我们特别关注此类架构对消费者、提供方及平台之间效用分配的影响。本文构建了一个包含算法选择的推荐生态系统模型，并分析了该设计下的多种可能结果。
原文摘要 · Abstract (English)
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.
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