ContextGNN goes to Elliot: Towards Benchmarking Relational Deep Learning for Static Link Prediction (aka Personalized Item Recommendation)

• URL: http://arxiv.org/abs/2503.16661v1
• Date: Thu, 20 Mar 2025 19:17:09 GMT
• Title: ContextGNN goes to Elliot: Towards Benchmarking Relational Deep Learning for Static Link Prediction (aka Personalized Item Recommendation)
• Authors: Alejandro Ariza-Casabona, Nikos Kanakaris, Daniele Malitesta,
• Abstract summary: We run experiments on three standard recommendation datasets and against six state-of-the-art GNN-based recommender systems.<n>Preliminary tests for the more traditional static link prediction task on the popular Amazon Book have demonstrated how ContextGNN has still room for improvement.
• Score: 47.31312886129455
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Relational deep learning (RDL) settles among the most exciting advances in machine learning for relational databases, leveraging the representational power of message passing graph neural networks (GNNs) to derive useful knowledge and run predicting tasks on tables connected through primary-to-foreign key links. The RDL paradigm has been successfully applied to recommendation lately, through its most recent representative deep learning architecture namely, ContextGNN. While acknowledging ContextGNN's improved performance on real-world recommendation datasets and tasks, preliminary tests for the more traditional static link prediction task (aka personalized item recommendation) on the popular Amazon Book dataset have demonstrated how ContextGNN has still room for improvement compared to other state-of-the-art GNN-based recommender systems. To this end, with this paper, we integrate ContextGNN within Elliot, a popular framework for reproducibility and benchmarking analyses, counting around 50 state-of-the-art recommendation models from the literature to date. On such basis, we run preliminary experiments on three standard recommendation datasets and against six state-of-the-art GNN-based recommender systems, confirming similar trends to those observed by the authors in their original paper. The code is publicly available on GitHub: https://github.com/danielemalitesta/Rel-DeepLearning-RecSys.

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