3 papers
cs.IR2026
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…
cs.IR2025
The Environmental Impact of Ensemble Techniques in Recommender Systems
Jannik Nitschke
Ensemble techniques in recommender systems have demonstrated accuracy improvements of 10-30%, yet their environmental impact remains unmeasured. While deep learning recommendation…
cs.LG2024
Evaluating the performance-deviation of itemKNN in RecBole and LensKit
Michael Schmidt, Jannik Nitschke, Tim Prinz
This study examines the performance of item-based k-Nearest Neighbors (ItemKNN) algorithms in the RecBole and LensKit recommender system libraries. Using four data sets (Anime, Mod…