On the Impact of Graph Neural Networks in Recommender Systems: A Topological Perspective

TL;DR

This paper offers a topological perspective on how graph neural networks improve recommender systems by analyzing dataset structures and model architectures, providing a formal taxonomy and an explanatory framework.

Contribution

It introduces a formal taxonomy of GNN-based recommendation models, formalizes dataset topological characteristics, and links these properties to model performance insights.

Findings

GNNs outperform traditional CF methods in many cases.

Dataset topology significantly influences GNN performance.

A unified framework helps understand GNN advantages in recommendations.

Abstract

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 the rapid adoption of graph neural networks (GNNs), which often outperform collaborative filtering (CF) methods such as latent factor models, deep neural networks, and generative strategies. Yet, despite their empirical success, the reasons why GNNs offer systematic advantages over other CF approaches remain only partially understood. This monograph advances a topology-centered perspective on GNN-based recommendation. We argue that a comprehensive understanding of these models' performance should consider the structural properties of user-item graphs and their interaction with GNN architectural design. To support this view, we introduce a formal taxonomy that distills common modeling patterns across eleven representative GNN-based recommendation approaches and consolidates them into a unified conceptual pipeline. We further formalize thirteen classical and topological characteristics of recommendation datasets and reinterpret them through the lens of graph machine learning. Using these definitions, we analyze the considered GNN-based recommender architectures to assess how and to what extent they encode such properties. Building on this analysis, we derive an explanatory framework that links measurable dataset characteristics to model behavior and performance. Taken together, this monograph re-frames GNN-based recommendation through its topological underpinnings and outlines open theoretical, data-centric, and evaluation challenges for the next generation of topology-aware recommender systems.