  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.
