From AutoRecSys to AutoRecLab: A Call to Build, Evaluate, and Govern Autonomous Recommender-Systems Research Labs

Joeran Beel (University of Siegen, Germany) Bela Gipp (University of Göttingen, Germany) Tobias Vente (University of Antwerp, Belgium) Moritz Baumgart (University of Siegen, Germany) Philipp Meister (University of Göttingen, Germany) Pre-Print @misc{beel2025autorecsysautoreclabbuildevaluate, title={From AutoRecSys to AutoRecLab: A Call to Build, Read more…

Tobias Vente presents the APS Explorer at ACM Recsys’25: Navigating Algorithm Performance Spaces for Informed Dataset Selection

We are excited to share that our PhD student, Tobias Vente, presented our research paper, “APS Explorer: Navigating Algorithm Performance Spaces for Informed Dataset Selection”, at the ACM RecSys 2025 conference held at the O2 Universum Convention Center in Prague, Czechia The Problem: Why RecSys Needs Better Read more…

Green Recommender Systems: Down-Sampling Datasets for Energy-Efficient Algorithm Performance

Abstract As recommender systems become increasingly prevalent, the environmental impact and energy efficiency of training these large-scale models have come under scrutiny. This paper investigates the potential for energy-efficient algorithm performance by optimizing dataset sizes through downsampling techniques. We conducted Read more…