10 citations · 23 across the 7 of their papers we have counts for
7 papers
Reproducibility in Machine Learning-based Research: Overview, Barriers and Drivers
Harald Semmelrock, Tony Ross-Hellauer, Simone Kopeinik +4
Many research fields are currently reckoning with issues of poor levels of reproducibility. Some label it a "crisis", and research employing or building Machine Learning (ML) model…
Reproducibility in Machine Learning-Driven Research
Harald Semmelrock, Simone Kopeinik, Dieter Theiler +2
Research is facing a reproducibility crisis, in which the results and findings of many studies are difficult or even impossible to reproduce. This is also the case in machine learn…
Uptrendz: API-Centric Real-time Recommendations in Multi-Domain Settings
Emanuel Lacic, Tomislav Duricic, Leon Fadljevic +2
In this work, we tackle the problem of adapting a real-time recommender system to multiple application domains, and their underlying data models and customization requirements. To…
Towards Employing Recommender Systems for Supporting Data and Algorithm Sharing
Peter Müllner, Stefan Schmerda, Dieter Theiler +2
Data and algorithm sharing is an imperative part of data and AI-driven economies. The efficient sharing of data and algorithms relies on the active interplay between users, data pr…
Using the Open Meta Kaggle Dataset to Evaluate Tripartite Recommendations in Data Markets
Dominik Kowald, Matthias Traub, Dieter Theiler +5
This work addresses the problem of providing and evaluating recommendations in data markets. Since most of the research in recommender systems is focused on the bipartite relations…
Evaluating Tag Recommendations for E-Book Annotation Using a Semantic Similarity Metric
Emanuel Lacic, Dominik Kowald, Dieter Theiler +4
In this paper, we present our work to support publishers and editors in finding descriptive tags for e-books through tag recommendations. We propose a hybrid tag recommendation sys…
AFEL-REC: A Recommender System for Providing Learning Resource Recommendations in Social Learning Environments
Dominik Kowald, Emanuel Lacic, Dieter Theiler +1
In this paper, we present preliminary results of AFEL-REC, a recommender system for social learning environments. AFEL-REC is build upon a scalable software architecture to provide…