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

arXiv: https://arxiv.org/abs/2503.16661 · HTML: https://mlsanity.com/paper/2503.16661/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 12 references that are in the map.
- First posted on arXiv 20 Mar 2025.

## Builds on (most cited first, 8 of 8)

- [LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation](https://mlsanity.com/paper/2002.02126/) (2020)
- [Neural Graph Collaborative Filtering](https://mlsanity.com/paper/1905.08108/) (2019)
- [Ups and Downs: Modeling the Visual Evolution of Fashion Trends with One-Class Collaborative Filtering](https://mlsanity.com/paper/1602.01585/) (2016)
- [Self-supervised Graph Learning for Recommendation](https://mlsanity.com/paper/2010.10783/) (2020)
- [Modeling User Exposure in Recommendation](https://mlsanity.com/paper/1510.07025/) (2015)
- [How Powerful is Graph Convolution for Recommendation?](https://mlsanity.com/paper/2108.07567/) (2021)
- [Elliot: a Comprehensive and Rigorous Framework for Reproducible Recommender Systems Evaluation](https://mlsanity.com/paper/2103.02590/) (2021)
- [ContextGNN: Beyond Two-Tower Recommendation Systems](https://mlsanity.com/paper/2411.19513/) (2024)

## Abstract (arXiv)

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.