research-article

Learning Graph-Based Geographical Latent Representation for Point-of-Interest Recommendation

Authors:

Gwanghoon Jang,

Jaewoo KangAuthors Info & Claims

CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management

Pages 135 - 144

https://doi.org/10.1145/3340531.3411905

Published: 19 October 2020 Publication History

Abstract

Several geographical latent representation models that capture geographical influences among points-of-interest (POIs) have been proposed. Although the models improve POI recommendation performance, they depend on shallow methods that cannot effectively capture highly non-linear geographical influences from complex user-POI networks. In this paper, we propose a new graph-based geographical latent representation model (GGLR) which can capture highly non-linear geographical influences from complex user-POI networks. Our proposed GGLR considers two types of geographical influences: ingoing influences and outgoing influences. Based on a graph auto-encoder, geographical latent representations of ingoing and outgoing influences are trained to increase geographical influences between two consecutive POIs that frequently appear in check-in histories. Furthermore, we propose a graph neural network-based POI recommendation model (GPR) that uses the trained geographical latent representations of ingoing and outgoing influences for the estimation of user preferences. In the experimental evaluation on real-world datasets, we show that GGLR effectively captures highly non-linear geographical influences and GPR achieves state-of-the-art performance.

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Index Terms

Learning Graph-Based Geographical Latent Representation for Point-of-Interest Recommendation
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Recommender systems
  2. World Wide Web
    1. Web searching and information discovery
      1. Collaborative filtering

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cover image ACM Conferences

CIKM '20: Proceedings of the 29th ACM International Conference on Information & Knowledge Management

October 2020

3619 pages

ISBN:9781450368599

DOI:10.1145/3340531

General Chairs:
Mathieu d'Aquin
DSI, Insight, NUI Galway, Ireland
,
Stefan Dietze
GESIS, Cologne, Germany, Heinrich-Heine-University Düsseldorf, Germany, L3S Research Center, Germany
,
Program Chairs:
Claudia Hauff
TU Delft, The Netherlands
,
Edward Curry
DSI, Insight, NUI Galway, Ireland
,
Philippe Cudre Mauroux
eXascale, University of Fribourg, Switzerland

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Published: 19 October 2020

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October 19 - 23, 2020

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Cited By

Zeng LChen XHuang PLuo KZhang XZhou Z(2024)Serving Graph Neural Networks With Distributed Fog Servers for Smart IoT ServicesIEEE/ACM Transactions on Networking10.1109/TNET.2023.329305232:1(550-565)Online publication date: Feb-2024
https://doi.org/10.1109/TNET.2023.3293052
Chen YXie NXu HChen XLee D(2024)A Multi-Context Aware Human Mobility Prediction Model Based on Motif-Preserving Travel Preference LearningIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.331428125:2(2139-2152)Online publication date: Feb-2024
https://doi.org/10.1109/TITS.2023.3314281
Sun XHuang BWang XYu D(2024)MARANExpert Systems with Applications: An International Journal10.1016/j.eswa.2023.121961238:PFOnline publication date: 15-Mar-2024
https://dl.acm.org/doi/10.1016/j.eswa.2023.121961
Fu JGao RYu YWu JLi JLiu DYe Z(2024)Contrastive graph learning long and short-term interests for POI recommendationExpert Systems with Applications10.1016/j.eswa.2023.121931238(121931)Online publication date: Mar-2024
https://doi.org/10.1016/j.eswa.2023.121931
Acharya MMohbey K(2024)High-order spatial connectivity mining over neural graph collaborative filtering for POI recommendation in location-based social networksEvolving Systems10.1007/s12530-024-09572-x15:4(1459-1474)Online publication date: 6-Mar-2024
https://doi.org/10.1007/s12530-024-09572-x
Cai JWang DChen HLiu CXiao Z(2024)Modeling dynamic spatiotemporal user preference for location prediction: a mutually enhanced methodWorld Wide Web10.1007/s11280-024-01245-827:2Online publication date: 13-Feb-2024
https://doi.org/10.1007/s11280-024-01245-8
Fan XHua YCao YZhao X(2023)Capturing Dynamic Interests of Similar Users for POI Recommendation Using Self-Attention MechanismSustainability10.3390/su1506503415:6(5034)Online publication date: 13-Mar-2023
https://doi.org/10.3390/su15065034
Corcoran PSpasić I(2023)Self-Supervised Representation Learning for Geographical Data—A Systematic Literature ReviewISPRS International Journal of Geo-Information10.3390/ijgi1202006412:2(64)Online publication date: 12-Feb-2023
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Liu JYi HGao YJing R(2023)Personalized Point-of-Interest Recommendation Using Improved Graph Convolutional Network in Location-Based Social NetworkElectronics10.3390/electronics1216349512:16(3495)Online publication date: 18-Aug-2023
https://doi.org/10.3390/electronics12163495
Wang DChen CDi CShu M(2023)Exploring Behavior Patterns for Next-POI Recommendation via Graph Self-Supervised LearningElectronics10.3390/electronics1208193912:8(1939)Online publication date: 20-Apr-2023
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