Lukas Wegmeth

Lukas Wegmeth
Ph.D. Student

Phone: +49 271 740-2591
Email: lukas.<last-name>@uni-siegen.de
Office: H-C 8318
Address: Office and postal address

Lukas Wegmeth is a Ph.D. Student of the Intelligent Systems Group at the University of Siegen. Before joining the ISG he completed his bachelor’s and master’s degree in Medical Computer Science at the University of Siegen. During his time as a graduate student, Lukas set his focus on the topic of Machine Learning and collaborated with different chairs of the University of Siegen to work on and release scientific research papers in the field.

Lukas will focus research towards his Ph.D. on Recommender Systems (RecSys) and Automated Machine Learning (AutoML).

Publications

2024

Wegmeth, Lukas; Vente, Tobias; Purucker, Lennart

Revealing the Hidden Impact of Top-N Metrics on Optimization in Recommender Systems Journal Article

In: 2024.

Abstract | Links | BibTeX

2023

Wegmeth, Lukas

Improving Recommender Systems Through the Automation of Design Decisions Proceedings Article

In: Proceedings of the 17th ACM Conference on Recommender Systems, pp. 1332-1338, 2023.

Abstract | Links | BibTeX

Wegmeth, Lukas; Vente, Tobias; Beel, Joeran

The Challenges of Algorithm Selection and Hyperparameter Optimization for Recommender Systems Journal Article

In: COSEAL Workshop 2023, 2023.

Links | BibTeX

Wegmeth, Lukas; Vente, Tobias; Purucker, Lennart; Beel, Joeran

The Effect of Random Seeds for Data Splitting on Recommendation Accuracy Proceedings Article

In: Proceedings of the 3rd Perspectives on the Evaluation of Recommender Systems Workshop, 2023.

Abstract | Links | BibTeX

2022

Wegmeth, Lukas; Beel, Joeran

CaMeLS: Cooperative Meta-Learning Service for Recommender Systems Proceedings Article

In: Proceedings of the 2nd Perspectives on the Evaluation of Recommender Systems Workshop, pp. 10–18, 2022.

Abstract | Links | BibTeX

Wegmeth, Lukas; Beel, Joeran

Cooperative Meta-Learning Service for Recommender Systems Journal Article

In: COSEAL Workshop 2022, 2022.

Links | BibTeX

Wegmeth, Lukas

The Impact of Feature Quantity on Recommendation Algorithm Performance: A Movielens-100K Case Study Proceedings Article

In: arXiv:2207.08713, 2022.

Abstract | Links | BibTeX