6 papers
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…
e-Fold Cross-Validation for Recommender-System Evaluation
Moritz Baumgart, Lukas Wegmeth, Tobias Vente +1
To combat the rising energy consumption of recommender systems we implement a novel alternative for k-fold cross validation. This alternative, named e-fold cross validation, aims t…
Green Recommender Systems: Optimizing Dataset Size for Energy-Efficient Algorithm Performance
Ardalan Arabzadeh, Tobias Vente, Joeran Beel
As recommender systems become increasingly prevalent, the environmental impact and energy efficiency of training large-scale models have come under scrutiny. This paper investigate…
From Theory to Practice: Implementing and Evaluating e-Fold Cross-Validation
Christopher Mahlich, Tobias Vente, Joeran Beel
This paper introduces e-fold cross-validation, an energy-efficient alternative to k-fold cross-validation. It dynamically adjusts the number of folds based on a stopping criterion.…