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

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  • Data Management and Data Mining
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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.

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Author information

Authors and Affiliations

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

    Pu Wang  (王 璞), Wei Chen  (陈 伟), Peng-Peng Zhao  (赵朋朋) & Lei Zhao  (赵 雷)

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

    Jian-Jiang Lu  (陆剑江)

Authors
  1. Pu Wang  (王 璞)
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  4. Peng-Peng Zhao  (赵朋朋)
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  5. Lei Zhao  (赵 雷)
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Corresponding author

Correspondence to Lei Zhao  (赵 雷).

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Conflict of Interest The authors declare that they have no conflict of interest.

Additional information

This work was supported by the National Natural Science Foundation of China under Grant No. 61876117.

Pu Wang is currently a senior engineer at the Department of Data Resource and Information Development, Soochow University, Suzhou. He received his Ph.D. degree in computer science from Soochow University, Suzhou, in 2021. His research interests include spatio-temporal database, smart transportation, deep learning, and knowledge graph.

Jian-Jiang Lu is a senior engineer at the Department of Data Resource and Information Development, Soochow University, Suzhou. He received his Ph.D. degree in computer science from Soochow University, Suzhou, in 2005. His research interests include recommender system, parallel streaming analytics, and network embedding.

Wei Chen is an associate professor at the School of Computer Science and Technology, Soochow University, Suzhou. He received his Ph.D. degree in computer science from Soochow University, Suzhou, in 2018. His research interests include heterogeneous information network analysis, crossplatform linkage and recommendation, and knowledge graph embedding and refinement.

Peng-Peng Zhao is a professor with the School of Computer Science and Technology, Soochow University, Suzhou. He received his Ph.D. degree in computer science from Soochow University, Suzhou, in 2008. His current research interests include data mining, deep learning, big data analysis, and recommender systems.

Lei Zhao is a professor with the School of Computer Science and Technology at Soochow University, Suzhou. He received his Ph.D. degree in computer science from Soochow University, Suzhou, in 2006. His recent research is to analyze large graph database in an effective, efficient, and secure way.

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Wang, P., Lu, JJ., Chen, W. et al. Spatio-Temporal Location Recommendation for Urban Facility Placement via Graph Convolutional and Recurrent Networks. J. Comput. Sci. Technol. 39, 1419–1440 (2024). https://doi.org/10.1007/s11390-023-2608-0

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  • DOI: https://doi.org/10.1007/s11390-023-2608-0

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