From community search to community understanding: A multimodal community query engine

• Main Authors: LI, Zhao, ZOU, Pengcheng, CHEN, Xia, HU, Shichang, ZHANG, Peng, ZHOU, Yumou, HE, Bingsheng, Yuchen LI, TANG, Xing
• Format: text
• Language: English
• Published: Institutional Knowledge at Singapore Management University 2021
• Subjects: Community Search; Community Understanding; Multimodal Search; Text Generation; Programming Languages and Compilers; Software Engineering
• Online Access: https://ink.library.smu.edu.sg/sis_research/6700; https://ink.library.smu.edu.sg/context/sis_research/article/7703/viewcontent/3459637.3481973.pdf
• Institution: Singapore Management University

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.