Towards Employing Recommender Systems for Supporting Data and Algorithm Sharing

no code implementations • 21 Oct 2022 • Peter Müllner, Stefan Schmerda, Dieter Theiler, Stefanie Lindstaedt, Dominik Kowald

We find that collaboration-based recommendations provide the most accurate recommendations in all scenarios.

Recommendation Systems

What Drives Readership? An Online Study on User Interface Types and Popularity Bias Mitigation in News Article Recommendations

no code implementations • 29 Nov 2021 • Emanuel Lacic, Leon Fadljevic, Franz Weissenboeck, Stefanie Lindstaedt, Dominik Kowald

In this paper, we discuss the introduction of personalized, content-based news recommendations on DiePresse, a popular Austrian online news platform, focusing on two specific aspects: (i) user interface type, and (ii) popularity bias mitigation.

Recommendation Systems

Evaluating Tag Recommendations for E-Book Annotation Using a Semantic Similarity Metric

no code implementations • 12 Aug 2019 • Emanuel Lacic, Dominik Kowald, Dieter Theiler, Matthias Traub, Lucky Kuffer, Stefanie Lindstaedt, Elisabeth Lex

Our idea is to mimic the vocabulary of users in Amazon, who search for and review e-books, and to combine these search terms with editor tags in a hybrid tag recommendation approach.

Descriptive Recommendation Systems +3

Using the Open Meta Kaggle Dataset to Evaluate Tripartite Recommendations in Data Markets

no code implementations • 12 Aug 2019 • Dominik Kowald, Matthias Traub, Dieter Theiler, Heimo Gursch, Emanuel Lacic, Stefanie Lindstaedt, Roman Kern, Elisabeth Lex

The presented work contributes to the tripartite recommendation problem in general and to the under-researched portfolio of evaluating recommender systems for data markets in particular.

Collaborative Filtering Recommendation Systems