39 citations · 47 across the 12 of their papers we have counts for
13 papers
SPADE: Escaping the Popularity-Similarity Frontier to Measure Serendipitous Recommendations
Tobias Vente, Maarten Peirsman, Noah Daniëls +2
Recommender systems engineer serendipity to foster active exploration and break predictable consumption cycles. The problem with existing offline beyond-accuracy metrics is that th…
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.…
EMERS: Energy Meter for Recommender Systems
Lukas Wegmeth, Tobias Vente, Alan Said +1
Due to recent advancements in machine learning, recommender systems use increasingly more energy for training, evaluation, and deployment. However, the recommender systems communit…
Recommender Systems Algorithm Selection for Ranking Prediction on Implicit Feedback Datasets
Lukas Wegmeth, Tobias Vente, Joeran Beel
The recommender systems algorithm selection problem for ranking prediction on implicit feedback datasets is under-explored. Traditional approaches in recommender systems algorithm…
From Clicks to Carbon: The Environmental Toll of Recommender Systems
Tobias Vente, Lukas Wegmeth, Alan Said +1
As global warming soars, the need to assess the environmental impact of research is becoming increasingly urgent. Despite this, few recommender systems research papers address thei…
Ensemble Boost: Greedy Selection for Superior Recommender Systems
Zainil Mehta, Tobias Vente
Ensemble techniques have demonstrated remarkable success in improving predictive performance across various domains by aggregating predictions from multiple models [1]. In the real…
The Potential of AutoML for Recommender Systems
Tobias Vente, Joeran Beel
Automated Machine Learning (AutoML) has greatly advanced applications of Machine Learning (ML) including model compression, machine translation, and computer vision. Recommender Sy…
Revealing the Hidden Impact of Top-N Metrics on Optimization in Recommender Systems
Lukas Wegmeth, Tobias Vente, Lennart Purucker
The hyperparameters of recommender systems for top-n predictions are typically optimized to enhance the predictive performance of algorithms. Thereby, the optimization algorithm, e…