SHOAL: Large-scale Hierarchical Taxonomy via Graph-based Query Coalition in E-commerce
Summary: SHOAL builds a large-scale, query-intent-driven hierarchical taxonomy by graph-based coalition of e-commerce searches, clustering hundreds of millions of items into interpretable shopping scenarios. It links these topics to ontology categories, achieving 98% placement precision and 5% CTR improvement in Alibaba deployment. (summarized by gpt-5.6-luna on Jul 24 2026)
Incoming Non-self Citations Over Time
Authors
- 1. Zhao Li (Alibaba)
- 2. Xia Chen (Alibaba; Southwest University)
- 3. Xuming Pan (Alibaba)
- 4. Pengcheng Zou (Alibaba)
- 5. Yuchen Li (Singapore Management University)
- 6. Guoxian Yu (Southwest University)
BibTeX Citation
@article{li_vldb19,
title = {{SHOAL: Large-scale Hierarchical Taxonomy via Graph-based Query Coalition in E-commerce}},
author = {Li, Zhao and Chen, Xia and Pan, Xuming and Zou, Pengcheng and Li, Yuchen and Yu, Guoxian},
journal = {PVLDB},
series = {{VLDB} '19},
volume = {12},
number = {12},
pages = {1858--1861},
doi = {10.14778/3352063.3352084},
url = {https://doi.org/10.14778/3352063.3352084},
year = {2019}
}Incoming Citations (Sorted by Pagerank)
Showing 1 of 1 citing papers.
| Rank | Citing Paper | Year | Venue | Pagerank |
|---|---|---|---|---|
| 11,816 | HDAG-Explorer: A System for Hierarchical DAG Summarization and Exploration | 2020 | VLDB | 5.093636e-05 |
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Outgoing Citations (Sorted by Pagerank)
Showing 0 of 0 cited papers.
Citations counted here include only citations to other VLDB/SIGMOD/CIDR/PODS papers in this database.
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|---|
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