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| Natura: | Preprint |
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2025
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| Accesso online: | https://arxiv.org/abs/2509.09414 |
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| _version_ | 1866911150144225280 |
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| author | Said, Alan Pera, Maria Soledad Ekstrand, Michael D. |
| author_facet | Said, Alan Pera, Maria Soledad Ekstrand, Michael D. |
| contents | In 2011, Xavier Amatriain sounded the alarm: recommender systems research was "doing it all wrong" [1]. His critique, rooted in statistical misinterpretation and methodological shortcuts, remains as relevant today as it was then. But rather than correcting course, we added new layers of sophistication on top of the same broken foundations. This paper revisits Amatriain's diagnosis and argues that many of the conceptual, epistemological, and infrastructural failures he identified still persist, in more subtle or systemic forms. Drawing on recent work in reproducibility, evaluation methodology, environmental impact, and participatory design, we showcase how the field's accelerating complexity has outpaced its introspection. We highlight ongoing community-led initiatives that attempt to shift the paradigm, including workshops, evaluation frameworks, and calls for value-sensitive and participatory research. At the same time, we contend that meaningful change will require not only new metrics or better tooling, but a fundamental reframing of what recommender systems research is for, who it serves, and how knowledge is produced and validated. Our call is not just for technical reform, but for a recommender systems research agenda grounded in epistemic humility, human impact, and sustainable practice. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_09414 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | We're Still Doing It (All) Wrong: Recommender Systems, Fifteen Years Later Said, Alan Pera, Maria Soledad Ekstrand, Michael D. Information Retrieval Artificial Intelligence In 2011, Xavier Amatriain sounded the alarm: recommender systems research was "doing it all wrong" [1]. His critique, rooted in statistical misinterpretation and methodological shortcuts, remains as relevant today as it was then. But rather than correcting course, we added new layers of sophistication on top of the same broken foundations. This paper revisits Amatriain's diagnosis and argues that many of the conceptual, epistemological, and infrastructural failures he identified still persist, in more subtle or systemic forms. Drawing on recent work in reproducibility, evaluation methodology, environmental impact, and participatory design, we showcase how the field's accelerating complexity has outpaced its introspection. We highlight ongoing community-led initiatives that attempt to shift the paradigm, including workshops, evaluation frameworks, and calls for value-sensitive and participatory research. At the same time, we contend that meaningful change will require not only new metrics or better tooling, but a fundamental reframing of what recommender systems research is for, who it serves, and how knowledge is produced and validated. Our call is not just for technical reform, but for a recommender systems research agenda grounded in epistemic humility, human impact, and sustainable practice. |
| title | We're Still Doing It (All) Wrong: Recommender Systems, Fifteen Years Later |
| topic | Information Retrieval Artificial Intelligence |
| url | https://arxiv.org/abs/2509.09414 |