Decoupled Recommender Systems: Exploring Alternative Recommender Ecosystem Designs
Fuente:
arXiv
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| Autori principali: | , , |
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| Natura: | Preprint |
| Pubblicazione: |
2025
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| _version_ | 1866909526911877120 |
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| author | Buhayh, Anas McKinnie, Elizabeth Burke, Robin |
| author_facet | Buhayh, Anas McKinnie, Elizabeth Burke, Robin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_03606 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Decoupled Recommender Systems: Exploring Alternative Recommender Ecosystem Designs Buhayh, Anas McKinnie, Elizabeth Burke, Robin Information Retrieval Artificial Intelligence Human-Computer Interaction 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. |
| title | Decoupled Recommender Systems: Exploring Alternative Recommender Ecosystem Designs |
| topic | Information Retrieval Artificial Intelligence Human-Computer Interaction |
| url | https://arxiv.org/abs/2503.03606 |