  Graph Neural Network (GNN) is the trending solution for item retrieval in
recommendation problems. Most recent reports, however, focus heavily on new
model architectures. This may bring some gaps when applying GNN in the
industrial setup, where, besides the model, constructing the graph and handling
data sparsity also play critical roles in the overall success of the project.
In this work, we report how GNN is applied for large-scale e-commerce item
retrieval at Shopee. We introduce our simple yet novel and impactful techniques
in graph construction, modeling, and handling data skewness. Specifically, we
construct high-quality item graphs by combining strong-signal user behaviors
with high-precision collaborative filtering (CF) algorithm. We then develop a
new GNN architecture named LightSAGE to produce high-quality items' embeddings
for vector search. Finally, we design multiple strategies to handle cold-start
and long-tail items, which are critical in an advertisement (ads) system. Our
models bring improvement in offline evaluations, online A/B tests, and are
deployed to the main traffic of Shopee's Recommendation Advertisement system.
