mlsanityData to 7 Oct 2026

ZOOMER: Boosting Retrieval on Web-scale Graphs by Regions of Interest

arXiv 2203.12596 ↗First posted 20 Mar 2022cs.IR cs.AI cs.LGcited by 4

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Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Builds on 27 of its 45 references that are in the map. First posted on arXiv 20 Mar 2022.

Builds on

27 of 45 references.

References · 27

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15 more
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Citing papers per year: 2022: 4.

4 papers.

  1. 2022Graph Attention Multi-Layer Perceptroncited by 82
  2. 2022Model Degradation Hinders Deep Graph Neural Networkscited by 26
  3. 2022NAFS: A Simple yet Tough-to-beat Baseline for Graph Representation Learningcited by 11
  4. 2022DFG-NAS: Deep and Flexible Graph Neural Architecture Searchcited by 1