A Dual Heterogeneous Graph Attention Network to Improve Long-Tail Performance for Shop Search in E-Commerce-Reference-Cited by-同舟云学术

A Dual Heterogeneous Graph Attention Network to Improve Long-Tail Performance for Shop Search in E-Commerce

Published:2020-07-06 Issue: Volume: Page:
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Container-title:Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining
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Author:

Niu Xichuan¹,Li Bofang²,Li Chenliang¹,Xiao Rong²,Sun Haochuan²,Deng Hongbo²,Chen Zhenzhong¹

Affiliation:

1. Wuhan University, Wuhan, China

2. Alibaba Group, Hangzhou, China

Funder

National Natural Science Foundation of China

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3394486.3403393

Reference31 articles.

1. Qingyao Ai Daniel N Hill SVN Vishwanathan and W Bruce Croft. 2019. A zero attention model for personalized product search. In CIKM. 379--388. Qingyao Ai Daniel N Hill SVN Vishwanathan and W Bruce Croft. 2019. A zero attention model for personalized product search. In CIKM. 379--388.

2. Qingyao Ai Yongfeng Zhang Keping Bi Xu Chen and W Bruce Croft. 2017. Learning a hierarchical embedding model for personalized product search. In SIGIR. 645--654. Qingyao Ai Yongfeng Zhang Keping Bi Xu Chen and W Bruce Croft. 2017. Learning a hierarchical embedding model for personalized product search. In SIGIR. 645--654.

3. Liheng Chen Yanru Qu Zhenghui Wang Lin Qiu Weinan Zhang Ken Chen Shaodian Zhang and Yong Yu. 2019. Sampled in Pairs and Driven by Text: A New Graph Embedding Framework. In WWW. 2644--2651. Liheng Chen Yanru Qu Zhenghui Wang Lin Qiu Weinan Zhang Ken Chen Shaodian Zhang and Yong Yu. 2019. Sampled in Pairs and Driven by Text: A New Graph Embedding Framework. In WWW. 2644--2651.

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