ACM RecSys 2026 – ISG Siegen

A RecSys Paper for $20: Why We Must Build, Evaluate, and Govern Autonomous Recommender Systems Research Labs (AutoRecLabs)

Joeran Beel1,2, Bela Gipp3, Tobias Vente4, Moritz Baumgart1,3, Philipp Meister3 and Sinan Pourazari1 1 University of Siegen, Siegen, Germany2 Recommender-Systems.com, Düsseldorf, Germany3 University of Göttingen, Göttingen, Germany4 University of Antwerp, Antwerp, Belgium FRAME’26: Methodology First – Rethinking Research Assessment in RecSys Workshop, September 28, 2026, Minneapolis, Minnesota, USA. Abstract Recommender Read more…

By Joeran Beel, 3 weeks28th September 2026 ago

Academic Community

“If AI can run an AI or RecSys experiment for $1, why spend four years doing a PhD?” — Keynote and Mentor at the ACM RecSys Doctoral Consortium 2026

My keynote at the Doctoral Consortium of ACM RecSys 2026 in Minneapolis On 27 September 2026, I gave a keynote at the Doctoral Consortium of ACM RecSys 2026 in Minneapolis. The title was deliberately provocative: “When AI Does RecSys Experiments for $1: Why Spend Four Years on a PhD?” The Read more…

By Joeran Beel, 3 weeks ago

Publications

FINALLY: A Dataset Recommender for RecSys Experiments

Abstract. FINALLY is a web-based recommender system for dataset selection in RecSys experiments. It supports a decision often made manually or by convention: choosing datasets to evaluate a recommendation algorithm offline. With FINALLY, users choose seed datasets, filters, a target set size, and a recommendation strategy. FINALLY returns dataset recommendations Read more…

By Joeran Beel, 2 months23rd August 2026 ago