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Regular Paper

Spatio-Temporal Location Recommendation for Urban Facility Placement via Graph Convolutional and Recurrent Networks

Pu Wang^¹, Jian-Jiang Lu^², Wei Chen^¹, Peng-Peng Zhao^¹, Lei Zhao^¹()

1Institute of Artificial Intelligence, School of Computer Science and Technology, Soochow University, Suzhou 215006, China

2Department of Data Resource and Information Development, Soochow University, Suzhou 215006, China

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Abstract

The ability to recommend candidate locations for service facility placement is crucial for the success of urban planning. Whether a location is suitable for establishing new facilities is largely determined by its potential popularity. However, it is a non-trivial task to predict popularity of candidate locations due to three significant challenges: 1) the spatio-temporal behavior correlations of urban dwellers, 2) the spatial correlations between candidate locations and existing facilities, and 3) the temporal auto-correlations of locations themselves. To this end, we propose a novel semi-supervised learning model, Spatio-Temporal Graph Convolutional and Recurrent Networks (STGCRN), aiming for popularity prediction and location recommendation. Specifically, we first partition the urban space into spatial neighborhood regions centered by locations, extract the corresponding features, and develop the location correlation graph. Next, a contextual graph convolution module based on the attention mechanism is introduced to incorporate local and global spatial correlations among locations. A recurrent neural network is proposed to capture temporal dependencies between locations. Furthermore, we adopt a location popularity approximation block to estimate the missing popularity from both the spatial and temporal domains. Finally, the overall implicit characteristics are concatenated and then fed into the recurrent neural network to obtain the ultimate popularity. The extensive experiments on two real-world datasets demonstrate the superiority of the proposed model compared with state-of-the-art baselines.

Keywords

location recommendation popularity prediction spatio-temporal correlation deep learning

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Journal of Computer Science and Technology

Volume 39 Issue 6,
November 2024

Pages 1419-1440

DOI: 10.1007/s11390-023-2608-0

Cite this article:

Wang P, Lu J-J, Chen W, et al. Spatio-Temporal Location Recommendation for Urban Facility Placement via Graph Convolutional and Recurrent Networks. Journal of Computer Science and Technology, 2024, 39(6): 1419-1440. https://doi.org/10.1007/s11390-023-2608-0