Publication Type
Conference Proceeding Article
Version
acceptedVersion
Publication Date
11-2021
Abstract
In this demo, we present an online multi-modal community query engine (MQE1 ) on Alibaba’s billion-scale heterogeneous network. MQE has two distinct features in comparison with existing community query engines. Firstly, MQE supports multimodal community search on heterogeneous graphs with keyword and image queries. Secondly, to facilitate community understanding in real business scenarios, MQE generates natural language descriptions for the retrieved community in combination with other useful demographic information. The distinct features of MQE benefit many downstream applications in Alibaba’s e-commerce platform like recommendation. Our experiments confirm the effectiveness and efficiency of MQE on graphs with billions of edges.
Keywords
Community Search, Community Understanding, Multimodal Search, Text Generation
Discipline
Programming Languages and Compilers | Software Engineering
Research Areas
Intelligent Systems and Optimization
Publication
Proceedings of the 30th ACM International Conference on Information & Knowledge Management, Virtual Conference, 2021 November 1-5
First Page
1
Last Page
5
Identifier
10.1145/3459637.3481973
Publisher
ACM
City or Country
Virtual Conference
Citation
LI, Zhao; ZOU, Pengcheng; CHEN, Xia; HU, Shichang; ZHANG, Peng; ZHOU, Yumou; HE, Bingsheng; Yuchen LI; and TANG, Xing.
From community search to community understanding: A multimodal community query engine. (2021). Proceedings of the 30th ACM International Conference on Information & Knowledge Management, Virtual Conference, 2021 November 1-5. 1-5.
Available at: https://ink.library.smu.edu.sg/sis_research/6700
Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial-No Derivative Works 4.0 International License.