39 citations · 48 across the 16 of their papers we have counts for
5 papers · 1 filter
From AutoRecSys to AutoRecLab: A Call to Build, Evaluate, and Govern Autonomous Recommender-Systems Research Labs
Joeran Beel, Bela Gipp, Tobias Vente +2
Recommender-systems research has accelerated model and evaluation advances, yet largely neglects automating the research process itself. We argue for a shift from narrow AutoRecSys…
Green Recommender Systems: Understanding and Minimizing the Carbon Footprint of AI-Powered Personalization
Lukas Wegmeth, Tobias Vente, Alan Said +1
As global warming soars, the need to assess and reduce the environmental impact of recommender systems is becoming increasingly urgent. Despite this, the recommender systems commun…
APS Explorer: Navigating Algorithm Performance Spaces for Informed Dataset Selection
Tobias Vente, Michael Heep, Abdullah Abbas +3
Dataset selection is crucial for offline recommender system experiments, as mismatched data (e.g., sparse interaction scenarios require datasets with low user-item density) can lea…
Algorithm Selection for Recommender Systems via Meta-Learning on Algorithm Characteristics
Jarne Mathi Decker, Joeran Beel
The Algorithm Selection Problem for recommender systems-choosing the best algorithm for a given user or context-remains a significant challenge. Traditional meta-learning approache…
Evaluating Sakana's AI Scientist: Bold Claims, Mixed Results, and a Promising Future?
Joeran Beel, Min-Yen Kan, Moritz Baumgart
A major step toward Artificial General Intelligence (AGI) and Super Intelligence is AI's ability to autonomously conduct research - what we term Artificial Research Intelligence (A…