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 | 13 | 2024 |
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 | 13 | 2023 |
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 | 12 | 2023 |
Green recommender systems: Optimizing dataset size for energy-efficient algorithm performance A Arabzadeh, T Vente, J Beel arXiv preprint arXiv:2410.09359, 2024 | 6 | 2024 |
EMERS: Energy Meter for Recommender Systems L Wegmeth, T Vente, A Said, J Beel RecSoGood: First International Workshop on Recommender Systems for …, 2024 | 5 | 2024 |
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 | 3 | 2024 |
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 | 3 | 2024 |
Best-Practices for Offline Evaluations of Recommender Systems J Beel, D Jannach, A Said, G Shani, T Vente, W Lukas Dagstuhl Seminar Report, 2024 | 2 | 2024 |
The Potential of AutoML for Recommender Systems T Vente, J Beel arXiv preprint arXiv:2402.04453, 2024 | 2 | 2024 |
Advancing Automation of Design Decisions in Recommender System Pipelines T Vente Doctoral Symposium at the ACM RecSys 2023 Conference, 2023 | 2 | 2023 |
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 | 1 | 2024 |
Ensemble Boost: Greedy Selection for Superior Recommender Systems Z Mehta, T Vente arXiv preprint arXiv:2407.05221, 2024 | 1 | 2024 |
Green Recommender Systems–A Call for Attention J Beel, A Said, T Vente, L Wegmeth Recommender-Systems. com Blog, 2024 | 1 | 2024 |
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 | 1 | 2024 |
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 |