21 citations · 43 across the 7 of their papers we have counts for
7 papers
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…
CryptoTL: Private, Efficient and Secure Transfer Learning
Roman Walch, Samuel Sousa, Lukas Helminger +3
Big data has been a pervasive catchphrase in recent years, but dealing with data scarcity has become a crucial question for many real-world deep learning (DL) applications. A popul…
What Drives Readership? An Online Study on User Interface Types and Popularity Bias Mitigation in News Article Recommendations
Emanuel Lacic, Leon Fadljevic, Franz Weissenboeck +2
Personalized news recommender systems support readers in finding the right and relevant articles in online news platforms. In this paper, we discuss the introduction of personalize…
SystemDS: A Declarative Machine Learning System for the End-to-End Data Science Lifecycle
Matthias Boehm, Iulian Antonov, Sebastian Baunsgaard +10
Machine learning (ML) applications become increasingly common in many domains. ML systems to execute these workloads include numerical computing frameworks and libraries, ML algori…
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…
Visualization of Co-Readership Patterns from an Online Reference Management System
Peter Kraker, Christian Schlögl, Kris Jack +1
In this paper, we analyze the adequacy and applicability of readership statistics recorded in social reference management systems for creating knowledge domain visualizations. Firs…