A Topology-aware Analysis of Graph Collaborative Filtering
Daniele Malitesta, Claudio Pomo, Vito Walter Anelli, Alberto Carlo, Maria Mancino, Eugenio Di Sciascio, Tommaso Di Noia

TL;DR
This paper investigates how the topological features of datasets influence the performance of graph neural network-based recommender systems, revealing significant dependencies and offering insights for better interpretation and design.
Contribution
It introduces a topology-aware analysis framework that links dataset topological features to recommendation accuracy in graph collaborative filtering.
Findings
Topological characteristics significantly affect recommendation accuracy.
Linear relationships exist between dataset features and model performance.
Statistical validation confirms the robustness of these dependencies.
Abstract
The successful integration of graph neural networks into recommender systems (RSs) has led to a novel paradigm in collaborative filtering (CF), graph collaborative filtering (graph CF). By representing user-item data as an undirected, bipartite graph, graph CF utilizes short- and long-range connections to extract collaborative signals that yield more accurate user preferences than traditional CF methods. Although the recent literature highlights the efficacy of various algorithmic strategies in graph CF, the impact of datasets and their topological features on recommendation performance is yet to be studied. To fill this gap, we propose a topology-aware analysis of graph CF. In this study, we (i) take some widely-adopted recommendation datasets and use them to generate a large set of synthetic sub-datasets through two state-of-the-art graph sampling methods, (ii) measure eleven of their…
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Taxonomy
TopicsAdvanced Graph Neural Networks · Recommender Systems and Techniques · Complex Network Analysis Techniques
MethodsLightGCN
