Joeran Beel

Meta-Learned Per-Instance Algorithm Selection in Scholarly Recommender Systems

2019 · arXiv

The effectiveness of recommender system algorithms varies in different real-world scenarios. It is difficult to choose a best algorithm for a scenario due to the quantity of algorithms available, and because of their varying performances. …

Online Evaluations for Everyone: Mr. DLib's Living Lab for Scholarly Recommendations

2018 · arXiv

We introduce the first 'living lab' for scholarly recommender systems. This lab allows recommender-system researchers to conduct online evaluations of their novel algorithms for scholarly recommendations, i.e., recommendations for research papers, citations, conferences, research grants, …

APS Explorer: Navigating Algorithm Performance Spaces for Informed Dataset Selection

2025

Dataset selection is crucial for offline recommender system experiments, as mismatched data (e.g., sparse interaction scenarios require datasets with low user-item density) can lead to unreliable results. Yet, 86\% of ACM RecSys 2024 papers provide …