Tommaso Di Noia

Graph models that manipulate the frequency spectrum of user-item interactions to separate preference signals from noise often report significant improvements, but concerns about evaluation rigor and reproducibility persist. We conduct a reproducibility and replicability study that examines three...

Graph Contrastive Learning (GCL) methods for recommendation learn representations by propagating signals over user-item interaction graphs. However, modeling these graphs with homogeneous edges can lead to semantic-structural misalignment, where information is exchanged between structurally adjac...