3 citations · 4 across the 4 of their papers we have counts for
12 papers
Recommendations in a Multi-Domain Setting: Adapting for Customization, Scalability and Real-Time Performance
Emanuel Lacic, Dominik Kowald
In this industry talk at ECIR'2022, we illustrate how to build a modern recommender system that can serve recommendations in real-time for a diverse set of application domains. Spe…
Popularity Bias in Collaborative Filtering-Based Multimedia Recommender Systems
Dominik Kowald, Emanuel Lacic
Multimedia recommender systems suggest media items, e.g., songs, (digital) books and movies, to users by utilizing concepts of traditional recommender systems such as collaborative…
Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering
Tomislav Duricic, Hussain Hussain, Emanuel Lacic +3
In this work, we study the utility of graph embeddings to generate latent user representations for trust-based collaborative filtering. In a cold-start setting, on three publicly a…
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…
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…