68 citations · 336 across the 43 of their papers we have counts for
8 papers · 1 filter
The Unfairness of Popularity Bias in Music Recommendation: A Reproducibility Study
Dominik Kowald, Markus Schedl, Elisabeth Lex
Research has shown that recommender systems are typically biased towards popular items, which leads to less popular items being underrepresented in recommendations. The recent work…
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…
The Impact of Time on Hashtag Reuse in Twitter: A Cognitive-Inspired Hashtag Recommendation Approach
Elisabeth Lex, Dominik Kowald
In our work [KPL17], we study temporal usage patterns of Twitter hashtags, and we use the Base-Level Learning (BLL) equation from the cognitive architecture ACT-R [An04] to model h…
Modeling Artist Preferences of Users with Different Music Consumption Patterns for Fair Music Recommendations
Dominik Kowald, Elisabeth Lex, Markus Schedl
Music recommender systems have become central parts of popular streaming platforms such as Last.fm, Pandora, or Spotify to help users find music that fits their preferences. These…
Should we Embed? A Study on the Online Performance of Utilizing Embeddings for Real-Time Job Recommendations
Markus Reiter-Haas, Emanuel Lacic, Tomislav Duricic +2
In this work, we present the findings of an online study, where we explore the impact of utilizing embeddings to recommend job postings under real-time constraints. On the Austrian…
Exploiting weak ties in trust-based recommender systems using regular equivalence
Tomislav Duricic, Emanuel Lacic, Dominik Kowald +1
User-based Collaborative Filtering (CF) is one of the most popular approaches to create recommender systems. CF, however, suffers from data sparsity and the cold-start problem sinc…
The TagRec Framework as a Toolkit for the Development of Tag-Based Recommender Systems
Dominik Kowald, Simone Kopeinik, Elisabeth Lex
Recommender systems have become important tools to support users in identifying relevant content in an overloaded information space. To ease the development of recommender systems,…