Location Recommendations Based on Multi-view Learning and Attention-Enhanced Graph Networks

Junxin Chen, Kuijie Lin, Xiang Chen*, Xijun Wang, Terng Yin Hsu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review


Personalized location recommendation plays a very important role in location-based social networks, from which both users and service providers can benefit from. In spite of the significant endeavors that have been made towards acquiring knowledge on location attributes and user inclinations, it is still faced with serious data sparsity problems. In this paper, we propose a personalized location recommendation model based on graph neural networks with multi-view learning to obtain effective representations of mobile users and locations from different heterogeneous graphs. We also design an attention-enhanced mechanism to explore the implicit interactions between mobile users and locations themselves. Conducting adequate comparative experiments on two real-world telecom datasets has demonstrated that our model achieves superior performance. Additionally, our model has been proven effective in addressing data sparsity issues.

Original languageEnglish
Title of host publicationBig Data and Social Computing - 8th China National Conference, BDSC 2023, Proceedings
EditorsXiaofeng Meng, Yang Chen, Liming Suo, Qi Xuan, Zi-Ke Zhang
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages13
ISBN (Print)9789819939244
StatePublished - 2023
Event8th China National Conference on Big Data and Social Computing, BDSC 2023 - Urumqi, China
Duration: 15 Jul 202317 Jul 2023

Publication series

NameCommunications in Computer and Information Science
Volume1846 CCIS
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937


Conference8th China National Conference on Big Data and Social Computing, BDSC 2023


  • Data Sparsity
  • Graph Neural Networks
  • Location Recommendation
  • Multi-view Learning


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