Lukas Wegmeth

According to our database, Lukas Wegmeth authored at least 19 papers between 2019 and 2025.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2025

The Potential of AutoML for Recommender Systems.

Tobias Vente, Lukas Wegmeth, Joeran Beel

Proceedings of the Adjunct Proceedings of the 33rd ACM Conference on User Modeling, 2025

Checky, the Paper-Submission Checklist Generator for Authors, Reviewers and LLMs.

Joeran Beel, Bela Gipp, Dietmar Jannach, Alan Said, Lukas Wegmeth, Tobias Vente

Proceedings of the Advances in Information Retrieval, 2025

2024

Green Recommender Systems: A Call for Attention.

Joeran Beel, Alan Said, Tobias Vente, Lukas Wegmeth

SIGIR Forum, December, 2024

e-Fold Cross-Validation for Recommender-System Evaluation.

Moritz Baumgart, Lukas Wegmeth, Tobias Vente, Joeran Beel

CoRR, 2024

EMERS: Energy Meter for Recommender Systems.

Lukas Wegmeth, Tobias Vente, Alan Said, Joeran Beel

CoRR, 2024

Greedy Ensemble Selection for Top-N Recommendations.

Tobias Vente, Zainil Mehta, Lukas Wegmeth, Joeran Beel

Proceedings of the Workshop Design, 2024

Removing Bad Influence: Identifying and Pruning Detrimental Users in Collaborative Filtering Recommender Systems.

Philipp Meister, Lukas Wegmeth, Tobias Vente, Joeran Beel

Proceedings of the Workshop Design, 2024

Recommender Systems Algorithm Selection for Ranking Prediction on Implicit Feedback Datasets.

Lukas Wegmeth, Tobias Vente, Joeran Beel

Proceedings of the 18th ACM Conference on Recommender Systems, 2024

From Clicks to Carbon: The Environmental Toll of Recommender Systems.

Tobias Vente, Lukas Wegmeth, Alan Said, Joeran Beel

Proceedings of the 18th ACM Conference on Recommender Systems, 2024

Informed Dataset Selection with 'Algorithm Performance Spaces'.

Joeran Beel, Lukas Wegmeth, Lien Michiels, Steffen Schulz

Proceedings of the 18th ACM Conference on Recommender Systems, 2024

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

Lukas Wegmeth, Tobias Vente, Lennart Purucker

Proceedings of the Advances in Information Retrieval, 2024

The Impact of Feature Quantity on Recommendation Algorithm Performance.

Lukas Wegmeth

Proceedings of The 32nd Irish Conference on Artificial Intelligence and Cognitive Science, 2024

2023

The Effect of Random Seeds for Data Splitting on Recommendation Accuracy.

Lukas Wegmeth, Tobias Vente, Lennart Purucker, Joeran Beel

Proceedings of the 3rd Workshop Perspectives on the Evaluation of Recommender Systems 2023 co-located with the 17th ACM Conference on Recommender Systems (RecSys 2023), 2023

Improving Recommender Systems Through the Automation of Design Decisions.

Lukas Wegmeth

Proceedings of the 17th ACM Conference on Recommender Systems, 2023

2022

A Machine Learning Framework for Automated Accident Detection Based on Multimodal Sensors in Cars.

Hawzhin Hozhabr Pour, Frédéric Li, Lukas Wegmeth, Christian Trense, Rafal Doniec, Marcin Grzegorzek, Roland Wismüller

Sensors, 2022

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

Lukas Wegmeth

CoRR, 2022

CaMeLS: Cooperative Meta-Learning Service for Recommender Systems.

Lukas Wegmeth, Joeran Beel

Proceedings of the Perspectives on the Evaluation of Recommender Systems Workshop 2022 co-located with the 16th ACM Conference on Recommender Systems (RecSys 2022), 2022

2021

Detecting Handwritten Mathematical Terms with Sensor Based Data.

Lukas Wegmeth, Alexander Hoelzemann, Kristof Van Laerhoven

CoRR, 2021

2019

Feature Extraction and Classification of Sensor Signals in Cars Based on a Modified Codebook Approach.

Hawzhin Hozhabr Pour, Lukas Wegmeth, Alexander Kordes, Marcin Grzegorzek, Roland Wismüller

Proceedings of the Progress in Computer Recognition Systems, 2019