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.

翻译：图神经网络（GNN）是推荐系统中商品检索的主流解决方案。然而，近期研究多聚焦于新型模型架构。在实际工业场景中应用GNN时，除模型设计外，图构建与数据稀疏性处理对项目整体成功同样至关重要，而现有研究对此关注不足。本文报告了如何在Shopee大规模电商商品检索中应用GNN，并介绍了我们在图构建、模型设计及数据偏斜处理方面的创新性实用技术：首先，通过融合强信号用户行为与高精度协同过滤（CF）算法，构建高质量商品图；其次，设计新型GNN架构LightSAGE，生成适用于向量搜索的高质量商品嵌入表示；最后，针对广告系统中关键的冷启动与长尾商品，提出多种处理策略。本模型在离线评估与在线A/B测试中均取得显著提升，并已部署至Shopee推荐广告系统的主流量中。