Interested in the impact of recommender systems on society? @elucherini4, @MatthewDSun, @aawinecoff, and I have software, papers, and a talk to share:
– T-RECS, a simulation tool for studying these questions github.com/elucherini/t-r…
– Accompanying paper arxiv.org/pdf/2107.08959… 🧵
– T-RECS, a simulation tool for studying these questions github.com/elucherini/t-r…
– Accompanying paper arxiv.org/pdf/2107.08959… 🧵
– A short piece on methodological concerns in simulation research arxiv.org/pdf/2107.14333…
– A talk (by me) offering a critical take on research on filter bubbles mediacentral.princeton.edu/media/1_45q6h2…
Here's a blog post that provides an overview and context: freedom-to-tinker.com/2021/08/04/stu…
– A talk (by me) offering a critical take on research on filter bubbles mediacentral.princeton.edu/media/1_45q6h2…
Here's a blog post that provides an overview and context: freedom-to-tinker.com/2021/08/04/stu…
The key rationale for this work is that phenomena such as algorithmic amplification of misinformation, filter bubbles, or content diversity in recommendations are difficult to study because they arise through repeated interactions between users, items, and the system over time.
Simulations can model how users, items, and algorithms interact over long timescales, and provide one valuable lens on these questions. This is a burgeoning line of research, and we hope to both systematize research in this area and provide foundational infrastructure.
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