39 citations · 48 across the 10 of their papers we have counts for
10 papers
Ensembles at Any Cost? Accuracy-Energy Trade-offs in Recommender Systems
Jannik Nitschke, Lukas Wegmeth, Joeran Beel
Ensemble methods are frequently used in recommender systems to improve accuracy by combining multiple models. Recent work reports sizable performance gains, but most studies still…
Green Recommender Systems: Understanding and Minimizing the Carbon Footprint of AI-Powered Personalization
Lukas Wegmeth, Tobias Vente, Alan Said +1
As global warming soars, the need to assess and reduce the environmental impact of recommender systems is becoming increasingly urgent. Despite this, the recommender systems commun…
e-Fold Cross-Validation for Recommender-System Evaluation
Moritz Baumgart, Lukas Wegmeth, Tobias Vente +1
To combat the rising energy consumption of recommender systems we implement a novel alternative for k-fold cross validation. This alternative, named e-fold cross validation, aims t…
EMERS: Energy Meter for Recommender Systems
Lukas Wegmeth, Tobias Vente, Alan Said +1
Due to recent advancements in machine learning, recommender systems use increasingly more energy for training, evaluation, and deployment. However, the recommender systems communit…
Recommender Systems Algorithm Selection for Ranking Prediction on Implicit Feedback Datasets
Lukas Wegmeth, Tobias Vente, Joeran Beel
The recommender systems algorithm selection problem for ranking prediction on implicit feedback datasets is under-explored. Traditional approaches in recommender systems algorithm…
From Clicks to Carbon: The Environmental Toll of Recommender Systems
Tobias Vente, Lukas Wegmeth, Alan Said +1
As global warming soars, the need to assess the environmental impact of research is becoming increasingly urgent. Despite this, few recommender systems research papers address thei…
The Potential of AutoML for Recommender Systems
Tobias Vente, Joeran Beel
Automated Machine Learning (AutoML) has greatly advanced applications of Machine Learning (ML) including model compression, machine translation, and computer vision. Recommender Sy…
Revealing the Hidden Impact of Top-N Metrics on Optimization in Recommender Systems
Lukas Wegmeth, Tobias Vente, Lennart Purucker
The hyperparameters of recommender systems for top-n predictions are typically optimized to enhance the predictive performance of algorithms. Thereby, the optimization algorithm, e…
The Impact of Feature Quantity on Recommendation Algorithm Performance: A Movielens-100K Case Study
Lukas Wegmeth
Recent model-based Recommender Systems (RecSys) algorithms emphasize on the use of features, also called side information, in their design similar to algorithms in Machine Learning…
Detecting Handwritten Mathematical Terms with Sensor Based Data
Lukas Wegmeth, Alexander Hoelzemann, Kristof Van Laerhoven
In this work we propose a solution to the UbiComp 2021 Challenge by Stabilo in which handwritten mathematical terms are supposed to be automatically classified based on time series…