  We introduce ZOOMER, a system deployed at Taobao, the largest e-commerce
platform in China, for training and serving GNN-based recommendations over
web-scale graphs. ZOOMER is designed for tackling two challenges presented by
the massive user data at Taobao: low training/serving efficiency due to the
huge scale of the graphs, and low recommendation quality due to the information
overload which distracts the recommendation model from specific user
intentions. ZOOMER achieves this by introducing a key concept, Region of
Interests (ROI) in GNNs for recommendations, i.e., a neighborhood region in the
graph with significant relevance to a strong user intention. ZOOMER narrows the
focus from the whole graph and "zooms in" on the more relevant ROIs, thereby
reducing the training/serving cost and mitigating the information overload at
the same time. With carefully designed mechanisms, ZOOMER identifies the
interest expressed by each recommendation request, constructs an ROI subgraph
by sampling with respect to the interest, and guides the GNN to reweigh
different parts of the ROI towards the interest by a multi-level attention
module. Deployed as a large-scale distributed system, ZOOMER supports graphs
with billions of nodes for training and thousands of requests per second for
serving. ZOOMER achieves up to 14x speedup when downsizing sampling scales with
comparable (even better) AUC performance than baseline methods. Besides, both
the offline evaluation and online A/B test demonstrate the effectiveness of
ZOOMER.
