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Tobias Vente
Tobias Vente
PhD Student, Intelligent Systems Group, University of Siegen, Germany
Dirección de correo verificada de student.uni-siegen.de - Página principal
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From Clicks to Carbon: The Environmental Toll of Recommender Systems
T Vente, L Wegmeth, A Said, J Beel
Proceedings of the 18th ACM Conference on Recommender Systems, 2024
132024
Introducing LensKit-Auto, an Experimental Automated Recommender System (AutoRecSys) Toolkit
T Vente, MD Ekstrand, J Beel
Demo Paper at the ACM RecSys 2023 Conference, 2023
132023
The Effect of Random Seeds for Data Splitting on Recommendation Accuracy
L Wegmeth, T Vente, L Purucker, J Beel
Proceedings of the 3rd Perspectives on the Evaluation of Recommender Systems …, 2023
122023
Green recommender systems: Optimizing dataset size for energy-efficient algorithm performance
A Arabzadeh, T Vente, J Beel
arXiv preprint arXiv:2410.09359, 2024
62024
EMERS: Energy Meter for Recommender Systems
L Wegmeth, T Vente, A Said, J Beel
RecSoGood: First International Workshop on Recommender Systems for …, 2024
52024
e-Fold Cross-Validation for Recommender-System Evaluation
M Baumgart, L Wegmeth, T Vente, J Beel
RecSoGood: First International Workshop on Recommender Systems for …, 2024
32024
From Theory to Practice: Implementing and Evaluating e-Fold Cross-Validation
C Mahlich, T Vente, J Beel
International Conference on Artificial Intelligence and Machine Learning …, 2024
32024
Best-Practices for Offline Evaluations of Recommender Systems
J Beel, D Jannach, A Said, G Shani, T Vente, W Lukas
Dagstuhl Seminar Report, 2024
22024
The Potential of AutoML for Recommender Systems
T Vente, J Beel
arXiv preprint arXiv:2402.04453, 2024
22024
Advancing Automation of Design Decisions in Recommender System Pipelines
T Vente
Doctoral Symposium at the ACM RecSys 2023 Conference, 2023
22023
e-Fold Cross-Validation for energy-aware Machine Learning Evaluations
C Mahlich, T Vente, J Beel
URL: https://arxiv. org/abs/2410.09463,(дата звернення: 29.10. 2024), 2024
12024
Ensemble Boost: Greedy Selection for Superior Recommender Systems
Z Mehta, T Vente
arXiv preprint arXiv:2407.05221, 2024
12024
Green Recommender Systems–A Call for Attention
J Beel, A Said, T Vente, L Wegmeth
Recommender-Systems. com Blog, 2024
12024
e-fold cross-validation: A computing and energy-efficient alternative to k-fold cross-validation with adaptive folds
J Beel, L Wegmeth, T Vente
OSF, 2024
12024
Optimal Dataset Size for Recommender Systems: Evaluating Algorithms' Performance via Downsampling
A Arabzadeh, J Beel, T Vente
arXiv preprint arXiv:2502.08845, 2025
2025
Sustainable Recommender Systems: Optimizing Dataset Size for Energy-Efficient Algorithm Performance
A Arabzadeh, T Vente, J Beel
RecSoGood: First International Workshop on Recommender Systems for …, 2024
2024
Removing Bad Influence: Identifying and Pruning Detrimental Users in Collaborative Filtering Recommender Systems
P Meister, L Wegmeth, T Vente, J Beel
RobustRecSys: Design, Evaluation and Deployment of Robust Recommender Systems, 2024
2024
Greedy Ensemble Selection for Top-N Recommendations
T Vente, Z Mehta, L Wegmeth, J Beel
RobustRecSys: Design, Evaluation and Deployment of Robust Recommender Systems, 2024
2024
Recommender Systems Algorithm Selection for Ranking Prediction on Implicit Feedback Datasets
L Wegmeth, T Vente, J Beel
Proceedings of the 18th ACM Conference on Recommender Systems, 2024
2024
Revealing the Hidden Impact of Top-N Metrics on Optimization in Recommender Systems
L Wegmeth, T Vente, L Purucker
European Conference on Information Retrieval, 140-156, 2024
2024
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