# Macro Graph Neural Networks for Online Billion-Scale Recommender Systems

arXiv: https://arxiv.org/abs/2401.14939 · HTML: https://mlsanity.com/paper/2401.14939/

## Key facts

- Cited by 40 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 26 Jan 2024.

## Built on by (most cited first, 30 of 40)

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- [How Do Recommendation Models Amplify Popularity Bias? An Analysis from the Spectral Perspective](https://mlsanity.com/paper/2404.12008/) (2024)
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- [PSL: Rethinking and Improving Softmax Loss from Pairwise Perspective for Recommendation](https://mlsanity.com/paper/2411.00163/) (2024)
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- [A Unified Framework for Combinatorial Optimization Based on Graph Neural Networks](https://mlsanity.com/paper/2406.13125/) (2024)
- [Uncertainty-Aware Graph Structure Learning](https://mlsanity.com/paper/2502.12618/) (2025)
- [Motif-Consistent Counterfactuals with Adversarial Refinement for Graph-Level Anomaly Detection](https://mlsanity.com/paper/2407.13251/) (2024)
- [Cost-efficient Knowledge-based Question Answering with Large Language Models](https://mlsanity.com/paper/2405.17337/) (2024)
- [ScaleGNN: Towards Scalable Graph Neural Networks via Adaptive High-order Neighboring Feature Fusion](https://mlsanity.com/paper/2504.15920/) (2025)
- [Macro Graph of Experts for Billion-Scale Multi-Task Recommendation](https://mlsanity.com/paper/2506.10520/) (2025)
- [Advancing Loss Functions in Recommender Systems: A Comparative Study with a R'enyi Divergence-Based Solution](https://mlsanity.com/paper/2506.15120/) (2025)
- [Better Late Than Never: Formulating and Benchmarking Recommendation Editing](https://mlsanity.com/paper/2406.04553/) (2024)
- [When Box Meets Graph Neural Network in Tag-aware Recommendation](https://mlsanity.com/paper/2406.12020/) (2024)
- [RIDA: A Robust Attack Framework on Incomplete Graphs](https://mlsanity.com/paper/2407.18170/) (2024)
- [Towards Graph Prompt Learning: A Survey and Beyond](https://mlsanity.com/paper/2408.14520/) (2024)
- [HGCL: Hierarchical Graph Contrastive Learning for User-Item Recommendation](https://mlsanity.com/paper/2505.19020/) (2025)
- [Customized Retrieval-Augmented Generation with LLM for Debiasing Recommendation Unlearning](https://mlsanity.com/paper/2511.05494/) (2025)
- [Guarding Graph Neural Networks for Unsupervised Graph Anomaly Detection](https://mlsanity.com/paper/2404.16366/) (2024)
- [Your Graph Recommender is Provably a Single-view Graph Contrastive Learning](https://mlsanity.com/paper/2407.17723/) (2024)
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## Abstract (arXiv)

Predicting Click-Through Rate (CTR) in billion-scale recommender systems poses a long-standing challenge for Graph Neural Networks (GNNs) due to the overwhelming computational complexity involved in aggregating billions of neighbors. To tackle this, GNN-based CTR models usually sample hundreds of neighbors out of the billions to facilitate efficient online recommendations. However, sampling only a small portion of neighbors results in a severe sampling bias and the failure to encompass the full spectrum of user or item behavioral patterns. To address this challenge, we name the conventional user-item recommendation graph as "micro recommendation graph" and introduce a more suitable MAcro Recommendation Graph (MAG) for billion-scale recommendations. MAG resolves the computational complexity problems in the infrastructure by reducing the node count from billions to hundreds. Specifically, MAG groups micro nodes (users and items) with similar behavior patterns to form macro nodes. Subsequently, we introduce tailored Macro Graph Neural Networks (MacGNN) to aggregate information on a macro level and revise the embeddings of macro nodes. MacGNN has already served Taobao's homepage feed for two months, providing recommendations for over one billion users. Extensive offline experiments on three public benchmark datasets and an industrial dataset present that MacGNN significantly outperforms twelve CTR baselines while remaining computationally efficient. Besides, online A/B tests confirm MacGNN's superiority in billion-scale recommender systems.