5 papers
On the Impact of Communities on Semi-supervised Classification Using Graph Neural Networks
Hussain Hussain, Tomislav Duricic, Elisabeth Lex +2
Graph Neural Networks (GNNs) are effective in many applications. Still, there is a limited understanding of the effect of common graph structures on the learning process of GNNs. I…
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…
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…
Trust-Based Collaborative Filtering: Tackling the Cold Start Problem 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. This approach is based on finding the most relevant k users from whose…