HMGCL: Heterogeneous multigraph contrastive learning for LBSN friend recommendation

Li, Yongkang1,2,3; Fan, Zipei1,4; Yin, Du1,2,3; Jiang, Renhe1,4; Deng, Jinliang1,5; Song, Xuan1,2,3

2022-10-01

10.1007/s11280-022-01092-5

WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS   Impact Factor & Quartile

1386-145X

1573-1413

Friend recommendation from user trajectory is a vital real-world application of location-based social networks (LBSN) services. Previous statistical analysis indicated that social network relationships could explain 10% to 30% of human movement, especially long-distance travel. Therefore, it is necessary to recognize patterns from human mobility to assist the friend recommendation. However, previous works either modelled friendships and check-in records by simple graphs with only one connection between any two nodes or ignored a large amount of vital spatio-temporal information and semantic information in raw LBSN data. To overcome the limitation of the simple graph commonly seen in previous works, we leverage heterogeneous multigraph to model LBSN data and define various semantic connections between nodes. Against this background, we propose a Heterogeneous Multigraph Contrastive Learning (HMGCL) model to capture spatio-temporal characteristics of human trajectories for user node embedding learning. Extensive experiments show that our method outperforms the state-of-the-art approaches in six real-world city datasets.

Heterogeneou multigraph Location-based social networks Contrastive learning Friend recommendation

SCI

English

First ; Corresponding

Japan's Ministry of Education, Culture, Sports, Science, and Technology (MEXT)[22H03573] ; National Key Research and Development Project of China[2021YFB1714400] ; Guangdong Provincial Key Laboratory[2020B121201001]

Computer Science

Computer Science, Information Systems ; Computer Science, Software Engineering

WOS:000864567300001

SPRINGER

COMPUTER SCIENCE

Web of Science

1.Southern Univ Sci & Technol, SUSTech UTokyo Joint Res Ctr Super Smart City, Dept Comp Sci & Engn, Shenzhen, Peoples R China
2.Southern Univ Sci & Technol, Dept Comp Sci & Engn, Guangdong Prov Key Lab Brain Inspired Intelligent, Shenzhen, Peoples R China
3.Southern Univ Sci & Technol, Res Inst Trustworthy Autonomous Syst, Shenzhen, Peoples R China
4.Univ Tokyo, Ctr Spatial Informat Sci, Tokyo, Japan
5.Univ Technol Sydney, Australian Artificial Intelligence Inst, Sydney, NSW, Australia

Li, Yongkang,Fan, Zipei,Yin, Du,et al. HMGCL: Heterogeneous multigraph contrastive learning for LBSN friend recommendation[J]. WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS,2022.

Li, Yongkang,Fan, Zipei,Yin, Du,Jiang, Renhe,Deng, Jinliang,&Song, Xuan.(2022).HMGCL: Heterogeneous multigraph contrastive learning for LBSN friend recommendation.WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS.

Li, Yongkang,et al."HMGCL: Heterogeneous multigraph contrastive learning for LBSN friend recommendation".WORLD WIDE WEB-INTERNET AND WEB INFORMATION SYSTEMS (2022).