Search Results for author:
Found 8 papers, 3 papers with code
Beyond-Accuracy: A Review on Diversity, Serendipity and Fairness in Recommender Systems Based on Graph Neural Networks
This review paper focuses on addressing these dimensions in GNN-based recommender systems, going beyond the conventional accuracy-centric perspective.
Uptrendz: API-Centric Real-time Recommendations in Multi-Domain Settings
In this work, we tackle the problem of adapting a real-time recommender system to multiple application domains, and their underlying data models and customization requirements.
Structack: Structure-based Adversarial Attacks on Graph Neural Networks
In this work, we study adversarial attacks that are uninformed, where an attacker only has access to the graph structure, but no information about node attributes.
On the Impact of Communities on Semi-supervised Classification Using Graph Neural Networks
In this work, we systematically study the impact of community structure on the performance of GNNs in semi-supervised node classification on graphs.
Empirical Comparison of Graph Embeddings for Trust-Based Collaborative Filtering
In this work, we study the utility of graph embeddings to generate latent user representations for trust-based collaborative filtering.
Should we Embed? A Study on the Online Performance of Utilizing Embeddings for Real-Time Job Recommendations
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.
Exploiting weak ties in trust-based recommender systems using regular equivalence
Such relationships typically form a very sparse trust network, which can be utilized to generate recommendations for users based on people they trust.
Trust-Based Collaborative Filtering: Tackling the Cold Start Problem Using Regular Equivalence
In our work, we explore the use of a measure from network science, i. e. regular equivalence, applied to a trust network to generate a similarity matrix that is used to select the k-nearest neighbors for recommending items.
