2025/03/05 by Anas Buhayh, Buhayh, Anas, Elizabeth McKinnie +3 · 2 citations
Computer Science · Social Sciences · #AI in Service Interactions #Artificial Intelligence (cs.AI) #Collaborative filtering #Dynamics (music) #Ecosystem #Ethics and Social Impacts of AI #FOS: Computer and information sciences #Human-Computer Interaction (cs.HC) #Information Retrieval (cs.IR) #Range (aeronautics) #Recommender Systems and Techniques #Recommender system #Subject (documents)
paper · pdf · doi:10.48550/arxiv.2503.03606
openalex publication_date 2025/03/05 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05
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.