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.

在 Shopee 的大规模电子商务商品检索中，我们采用了图神经网络（GNN）及其简单但创新且有影响力的技术，包括图构建、建模和处理数据偏斜，以提高离线评估和在线 A/B 测试，并将模型部署到 Shopee 推荐广告系统的主要流量中。

LightSAGE：Shopee 广告推荐中的大规模商品检索的图神经网络

LightSAGE: Graph Neural Networks for Large Scale Item Retrieval in  Shopee's Advertisement Recommendation

Lossy gradient compression, with either unbiased or biased compressors, has
become a key tool to avoid the communication bottleneck in centrally
coordinated distributed training of machine learning models. We analyze the
performance of two standard and general types of methods: (i) distributed
quantized SGD (D-QSGD) with arbitrary unbiased quantizers and (ii) distributed
SGD with error-feedback and biased compressors (D-EF-SGD) in the heterogeneous
(non-iid) data setting. Our results indicate that D-EF-SGD is much less
affected than D-QSGD by non-iid data, but both methods can suffer a slowdown if
data-skewness is high. We further study two alternatives that are not (or much
less) affected by heterogenous data distributions: first, a recently proposed
method that is effective on strongly convex problems, and secondly, we point
out a more general approach that is applicable to linear compressors only but
effective in all considered scenarios.

本文研究比较了两种标准的数据压缩方法：分布式量化 SGD 和分布式 SGD 反馈错误的压缩机在非独立同分布数据集的训练效果，结果表明，后者比前者更适合非独立同分布数据，但两种方法都会因为数据分布的不均匀而变慢。文中还提出一种对于强凸问题有效的方法和适用于线性压缩器的更通用的方法。