| 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 | 11 | 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 | 10 | 2023 |
| 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 | 9 | 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 |
| Green recommender systems: Optimizing dataset size for energy-efficient algorithm performance A Arabzadeh, T Vente, J Beel arXiv preprint arXiv:2410.09359, 2024 | 4 | 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 | 1 | 2024 |
| The Potential of AutoML for Recommender Systems T Vente, J Beel arXiv preprint arXiv:2402.04453, 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 |
| Advancing Automation of Design Decisions in Recommender System Pipelines T Vente Doctoral Symposium at the ACM RecSys 2023 Conference, 2023 | 1 | 2023 |
| e-Fold Cross-Validation for energy-aware Machine Learning Evaluations C Mahlich, T Vente, J Beel arXiv e-prints, arXiv: 2410.09463, 2024 | | 2024 |
| 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 |
| Ensemble Boost: Greedy Selection for Superior Recommender Systems Z Mehta, T Vente arXiv preprint arXiv:2407.05221, 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 |
| The Challenges of Algorithm Selection and Hyperparameter Optimization for Recommender Systems L Wegmeth, T Vente, J Beel COSEAL23 - COnfiguration and SElection of ALgorithms, 2023 | | 2023 |
| The Feasibility of Greedy Ensemble Selection for Automated Recommender Systems T Vente, L Purucker, J Beel COSEAL'22 - COnfiguration and SElection of ALgorithms - http://dx.doi.org/10 …, 2022 | | 2022 |
Joeran BeelUniversity of Siegen (previously NII Tokyo, Trinity College Dublin, UC Berkeley, ...)Verified email at berkeley.edu
