39 citations · 48 across the 16 of their papers we have counts for
17 papers
AutoRecLab: Describe the Experiment, Get the Code!
Moritz Baumgart, Philipp Meister, Justus Krell +3
Empirical evaluation is central to recommender-systems (RecSys) research, but turning experimental designs into executable code remains a manual and error-prone task. We present Au…
Ensembles at Any Cost? Accuracy-Energy Trade-offs in Recommender Systems
Jannik Nitschke, Lukas Wegmeth, Joeran Beel
Ensemble methods are frequently used in recommender systems to improve accuracy by combining multiple models. Recent work reports sizable performance gains, but most studies still…
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…
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…
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…
Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML
Lennart Purucker, Lennart Schneider, Marie Anastacio +3
Automated machine learning (AutoML) systems commonly ensemble models post hoc to improve predictive performance, typically via greedy ensemble selection (GES). However, we believe…
CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure
Lennart Purucker, Joeran Beel
Many state-of-the-art automated machine learning (AutoML) systems use greedy ensemble selection (GES) by Caruana et al. (2004) to ensemble models found during model selection post…
Assembled-OpenML: Creating Efficient Benchmarks for Ensembles in AutoML with OpenML
Lennart Purucker, Joeran Beel
Automated Machine Learning (AutoML) frameworks regularly use ensembles. Developers need to compare different ensemble techniques to select appropriate techniques for an AutoML fram…