  Recent advancements in Graph Neural Networks (GNN) have facilitated their
widespread adoption in various applications, including recommendation systems.
GNNs have proven to be effective in addressing the challenges posed by
recommendation systems by efficiently modeling graphs in which nodes represent
users or items and edges denote preference relationships. However, current GNN
techniques represent nodes by means of a single static vector, which may
inadequately capture the intricate complexities of users and items. To overcome
these limitations, we propose a solution integrating a cutting-edge model
inspired by category theory: Sheaf4Rec. Unlike single vector representations,
Sheaf Neural Networks and their corresponding Laplacians represent each node
(and edge) using a vector space. Our approach takes advantage from this theory
and results in a more comprehensive representation that can be effectively
exploited during inference, providing a versatile method applicable to a wide
range of graph-related tasks and demonstrating unparalleled performance. Our
proposed model exhibits a noteworthy relative improvement of up to 8.53% on
F1-Score@10 and an impressive increase of up to 11.29% on NDCG@10,
outperforming existing state-of-the-art models such as Neural Graph
Collaborative Filtering (NGCF), KGTORe and other recently developed GNN-based
models. In addition to its superior predictive capabilities, Sheaf4Rec shows
remarkable improvements in terms of efficiency: we observe substantial runtime
improvements ranging from 2.5% up to 37% when compared to other GNN-based
competitor models, indicating a more efficient way of handling information
while achieving better performance. Code is available at
https://github.com/antoniopurificato/Sheaf4Rec.
