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cs.IR2025
On the Impact of Graph Neural Networks in Recommender Systems: A Topological Perspective
Daniele Malitesta, Claudio Pomo, Vito Walter Anelli +3
In recommender systems, user-item interactions can be modeled as a bipartite graph, where user and item nodes are connected by undirected edges. This graph-based view has motivated…
cs.IR2025
ContextGNN goes to Elliot: Towards Benchmarking Relational Deep Learning for Static Link Prediction (aka Personalized Item Recommendation)
Alejandro Ariza-Casabona, Nikos Kanakaris, Daniele Malitesta
Relational deep learning (RDL) settles among the most exciting advances in machine learning for relational databases, leveraging the representational power of message passing graph…