Publications

Google Scholar, ResearchGate

Journals

1. Green Recommender Systems: Understanding and Minimizing the Carbon Footprint of AI-Powered Personalization
   L. Wegmeth*, T. Vente*, A. Said, J. Beel. ACM Transactions on Recommender Systems
   [ACM Digital Library, PDF, code]

Conferences

1. APS Explorer: Navigating Algorithm Performance Spaces for Informed Dataset Selection
   T. Vente, M. Heep, A. Abbas, T. Sperle, J. Beel, B. Goethals. ACM RecSys’25 (Demo Paper)
   [APS Explorer, ACM Digital Library, PDF, code]

2. Automated Learning of Software Configuration Spaces is not Easy
   M. Weiß, R. Müller, L. Güthing, T. Vente, L. Wegmeth, I. Schaefer, M. Lochau. ACM SPLC’25 (Full Paper)
   [ACM Digital Library, PDF]

3. Checky, the Paper-Submission Checklist Generator for Authors, Reviewers and LLMs
   J. Beel, B. Gipp, D. Jannach, A. Said, L. Wegmeth, T Vente. ECIR’25 (Demo Paper)
   [ACM Digital Library, PDF, code]

4. From Clicks to Carbon: The Environmental Toll of Recommender Systems
   T. Vente, L. Wegmeth, A. Said, J. Beel. ACM RecSys’24 (Full Paper, Reproducibility Track)
   [ACM Digital Library, PDF, code, ACM RecSys’25 presentation]

5. Recommender Systems Algorithm Selection for Ranking Prediction on Implicit Feedback Datasets
   L. Wegmeth, T. Vente, J. Beel. ACM RecSys’24 (LBR PAPER)
   [ACM Digital Library, PDF, code]

6. Revealing the Hidden Impact of Top-N Metrics on Optimization in Recommender Systems
   L. Wegmeth, T. Vente, L. Purucker. ECIR’24 (Full Paper)
   [ACM Digital Library, PDF, code]

7. From Theory to Practice: Implementing and Evaluating e-Fold Cross-Validation
   C. Mahlich, T. Vente, J. Beel CAIMLR’24 (Full Paper)
   [SPIE Digital Library, PDF, code]

8. Introducing LensKit-Auto, an Experimental Automated Recommender System (AutoRecSys) Toolkit
   T. Vente, M.D. Ekstrand, J. Beel. RecSys’23 (Demo Track)
   [ACM Digital Library, PDF, code, video]

9. Advancing Automation of Design Decisions in Recommender System Pipelines
   T. Vente. RecSys’23 (Doctoral Symposium)
   [ACM Digital Library]

Workshops, Tutorials, & Others

1. The Potential of AutoML for Recommender Systems
   T. Vente, L. Wegmeth, J. Beel. HyPer Workshop @ ACM UMAP’25 (Full Paper)
   [ACM Digital Library, code]

2. Greedy Ensemble Selection for Top-N Recommendations
   T. Vente, Z. Mehta, L. Wegmeth, J. Beel. RobustRecSys Workshop @ RecSys’24 (Full Paper)
   [CEUR Workshop Proceedings, PDF, code]

3. Removing Bad Influence: Identifying and Pruning Detrimental Users in Collaborative Filtering Recommender Systems
   P. Meister, L. Wegmeth, T. Vente, J. Beel. RobustRecSys Workshop @ RecSys’24 (Short Paper)
   [CEUR Workshop Proceedings, PDF, code]

4. EMERS: Energy Meter for Recommender Systems
   L. Wegmeth, T. Vente, A. Said, J. Beel. RecSoGood Workshop @ RecSys’24 (Short Paper)
   [Springer, PDF, code, video]

5. e-Fold Cross-Validation for Recommender-System Evaluation
   M. Baumgart, L. Wegmeth, T. Vente, J. Beel. RecSoGood Workshop @ RecSys’24 (Short Paper)
   [Springer, PDF]

6. Sustainable Recommender Systems: Optimizing Dataset Size for Energy-Efficient Algorithm Performance
   A. Arabzadeh, T. Vente, J. Beel. RecSoGood Workshop @ RecSys’24 (Short Paper)
   [Springer, PDF, code]

7. The Effect of Random Seeds for Data Splitting on Recommendation Accuracy
   L. Wegmeth, T. Vente, L. Purucker, J. Beel. PERSPECTIVES ‘23 (RecSys Workshop)
   [CEUR Workshop Proceedings, PDF, code, video]

8. The Challenges of Algorithm Selection and Hyperparameter Optimization for Recommender Systems
   L. Wegmeth, T. Vente, J.Beel COSEAL’23 (COnfiguration and SElection of ALgorithms Workshop)
   [poster]

9. The Feasibility of Greedy Ensemble Selection for Automated Recommender Systems
   T. Vente, L. Purucker, J. Beel. COSEAL’22 (COnfiguration and SElection of ALgorithms Workshop)
   [poster]