SSTP: Social and Spatial-Temporal Aware Next Point-of-Interest Recommendation

Data Science and Engineering

Research Paper

Yuhua Li¹  Yujing Zhang¹  Yixiong Zou¹  Ruixuan Li¹  Junzhuang Wu¹  Zhenyu Zhang¹ 

[1] School of Computer Science and Technology, Huazhong University of Science and Technology, 430074, Wuhan, Hubei, China;

关键词: Recommendation systems;    Location-based social networks;    Point-of-interest;    Attention mechanism;    Graph attention network;   

DOI  :  10.1007/s41019-023-00221-y

 received in 2023-05-17, accepted in 2023-08-07,  发布年份 2023

来源: Springer

【 摘 要 】

The expansion of available information in location-based social networks (LBSNs) has led to information overload, making it urgent to discover users’ next point-of-interest (POI). Some existing works only consider certain modal information in LBSNs and do not transform them into high-dimensional structures, which hinders the alleviation of the data sparsity problem. Moreover, many approaches rely solely on social relationships, making it difficult to recommend POIs to new users without association information. To tackle these challenges, we propose a social- and spatial–temporal-aware next point-of-Interest (SSTP) recommendation model. SSTP uses two feature encoders based on self-attention mechanism and gate recurrent unit to model users’ check-in enhancement sequence hierarchically. We also design a random neighborhood sampling approach to mine user social relationships, thus alleviating the user cold start problem. Finally, we propose a geographical-aware graph attention network to learn the sensitivity of users to distance. Extensive experiments on two real-world datasets show that SSTP outperforms state-of-the-art models, improving Hit@k by 2.26–6.55%\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\%$$\end{document} and MAP@k by 3.49–6.55%\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\%$$\end{document}. Moreover, SSTP has better performance on sparse data, with an average improvement of 6.09%\documentclass[12pt]{minimal}\usepackage{amsmath}\usepackage{wasysym}\usepackage{amsfonts}\usepackage{amssymb}\usepackage{amsbsy}\usepackage{mathrsfs}\usepackage{upgreek}\setlength{\oddsidemargin}{-69pt}\begin{document}$$\%$$\end{document} on the Hit@k. The code can be downloaded at https://github.com/Rih0/sstp.

【 授权许可】

CC BY   
© The Author(s) 2023

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