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(Author / Reviewer / Editor)
Wang P, Lu JJ, Chen W et al. Spatio-temporal location recommendation for urban facility placement via graph convolutional and recurrent networks. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 39(6): 1419−1440 Nov. 2024. DOI: 10.1007/s11390-023-2608-0.
Citation: Wang P, Lu JJ, Chen W et al. Spatio-temporal location recommendation for urban facility placement via graph convolutional and recurrent networks. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 39(6): 1419−1440 Nov. 2024. DOI: 10.1007/s11390-023-2608-0.

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

Funds: This work was supported by the National Natural Science Foundation of China under Grant No. 61876117.
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  • Author Bio:

    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

  • Corresponding author:

    zhaol@suda.edu.cn

  • Received Date: June 23, 2022
  • Accepted Date: March 06, 2023
  • 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.

  • [1]
    Karamshuk D, Noulas A, Scellato S, Nicosia V, Mascolo C. Geo-spotting: Mining online location-based services for optimal retail store placement. In Proc. the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug. 2013, pp.793–801. DOI: 10.1145/2487575.2487616.
    [2]
    Wang F, Chen L, Pan W. Where to place your next restaurant?: Optimal restaurant placement via leveraging user-generated reviews. In Proc. the 25th ACM International Conference on Information and Knowledge Management, Oct. 2016, pp.2371–2376. DOI: 10.1145/2983323.2983696.
    [3]
    Xu M, Wang T, Wu Z, Zhou J, Li J, Wu H. Demand driven store site selection via multiple spatial-temporal data. In Proc. the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Oct. 31–Nov. 3, 2016, Article No. 40. DOI: 10.1145/2996913.2996996.
    [4]
    Mitra S, Saraf P, Bhattacharya A. TIPS: Mining top-k locations to minimize user-inconvenience for trajectory-aware services. IEEE Trans. Knowledge and Data Engineering, 2021, 33(3): 1238–1250. DOI: 10.1109/TKDE.2019.2935448.
    [5]
    Hsieh H P, Lin F, Li C T, Yen I E H, Chen H Y. Temporal popularity prediction of locations for geographical placement of retail stores. Knowledge and Information Systems, 2019, 60(1): 247–273. DOI: 10.1007/s10115-018-1311-x.
    [6]
    Ma Y, Mao J, Ba Z, Li G. Location recommendation by combining geographical, categorical, and social preferences with location popularity. Information Processing & Management, 2020, 57(4): 102251. DOI: 10.1016/j.ipm.2020.102251.
    [7]
    Wang P, Chen W, Zhao L. Towards effective top-k location recommendation for business facility placement. In Proc. the 13th International Conference on Knowledge Science, Engineering and Management, Aug. 2020, pp.51–63. DOI: 10.1007/978-3-030-55393-7_5.
    [8]
    Wang P, Chen W, Huang J, Wei Y, Fang J, Zhao L. Location prediction for facility placement by incorporating multi-characteristic information. Intelligent Data Analysis, 2021, 25(5): 1187–1210. DOI: 10.3233/IDA-205420.
    [9]
    Zhou J, Cui G, Hu S, Zhang Z, Yang C, Liu Z, Wang L, Li C, Sun M. Graph neural networks: A review of methods and applications. AI Open, 2020, 1: 57–81. DOI: 10.1016/j.aiopen.2021.01.001.
    [10]
    Zaremba W, Sutskever I, Vinyals O. Recurrent neural network regularization. arXiv: 1409.2329, 2014. https://arxiv.org/abs/1409.2329, Sept. 2024.
    [11]
    Hochreiter S, Schmidhuber J. Long short-term memory. Neural Computation, 1997, 9(8): 1735–1780. DOI: 10.1162/neco.1997.9.8.1735.
    [12]
    Cho K, van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y. Learning phrase representations using RNN encoder-decoder for statistical machine translation. In Proc. the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), Oct. 2014, pp.1724–1734. DOI: 10.3115/v1/D14-1179.
    [13]
    Li X, Čeikute V, Jensen C S, Tan K L. Trajectory based optimal segment computation in road network databases. In Proc. the 21st ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Nov. 2013, pp.396–399. DOI: 10.1145/2525314.2525444.
    [14]
    Mitra S, Saraf P, Sharma R, Bhattacharya A, Ranu S, Bhandari H. NetClus: A scalable framework for locating top-k sites for placement of trajectory-aware services. In Proc. the 33rd International Conference on Data Engineering, Apr. 2017, pp.87–90. DOI: 10.1109/ICDE.2017.46.
    [15]
    Li Y, Bao J, Li Y, Wu Y, Gong Z, Zheng Y. Mining the most influential k-location set from massive trajectories. In Proc. the 24th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems, Oct. 31–Nov. 3, 2016, Article No. 51. DOI: 10.1145/2996913.2997009.
    [16]
    Ruan S, Bao J, Liang Y, Li R, He T, Meng C, Li Y, Wu Y, Zheng Y. Dynamic public resource allocation based on human mobility prediction. Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, 2020, 4(1): Article No. 25. DOI: 10.1145/3380986.
    [17]
    Pan Z, Wang Z, Wang W, Yu Y, Zhang J, Zheng Y. Matrix factorization for spatio-temporal neural networks with applications to urban flow prediction. In Proc. the 28th ACM International Conference on Information and Knowledge Management, Nov. 2019, pp.2683–2691. DOI: 10.1145/3357384.3357832.
    [18]
    Hsieh H P, Lin S D, Zheng Y. Inferring air quality for station location recommendation based on urban big data. In Proc. the 21st ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug. 2015, pp.437–446. DOI: 10.1145/2783258.2783344.
    [19]
    Zheng Y, Liu F, Hsieh H P. U-air: When urban air quality inference meets big data. In Proc. the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Aug. 2013, pp.1436–1444. DOI: 10.1145/2487575.2488188.
    [20]
    Liu Q, Wu S, Wang L, Tan T. Predicting the next location: A recurrent model with spatial and temporal contexts. In Proc. the 30th AAAI Conference on Artificial Intelligence, Feb. 2016, pp.194–200. DOI: 10.1609/aaai.v30i1.9971.
    [21]
    Luo Y, Liu Q, Liu Z. STAN: Spatio-temporal attention network for next location recommendation. In Proc. the 2021 Web Conference, Apr. 2021, pp.2177–2185. DOI: 10.1145/3442381.3449998.
    [22]
    Zheng B, Hu Q, Ming L, Hu J, Chen L, Zheng K, Jensen C S. SOUP: Spatial-temporal demand forecasting and competitive supply in transportation. IEEE Trans. Knowledge and Data Engineering, 2023, 35(2): 2034–2047. DOI: 10.1109/TKDE.2021.3110778.
    [23]
    Wang X, Ma Y, Wang Y, Jin W, Wang X, Tang J, Jia C, Yu J. Traffic flow prediction via spatial temporal graph neural network. In Proc. the 2020 Web Conference, Apr. 2020, pp.1082–1092. DOI: 10.1145/3366423.3380186.
    [24]
    Zhang W, Liu H, Liu Y, Zhou J, Xiong H. Semi-supervised hierarchical recurrent graph neural network for city-wide parking availability prediction. In Proc. the 34th AAAI Conference on Artificial Intelligence, Feb. 2020, pp.1186–1193. DOI: 10.1609/aaai.v34i01.5471.
    [25]
    Guo S, Lin Y, Feng N, Song C, Wan H. Attention based spatial-temporal graph convolutional networks for traffic flow forecasting. In Proc. the 33rd AAAI Conference on Artificial Intelligence, Jan. 27–Feb. 1, 2019, pp.922–929. DOI: 10.1609/aaai.v33i01.3301922.
    [26]
    Bai L, Yao L, Wang X, Li C, Zhang X. Deep spatial-temporal sequence modeling for multi-step passenger demand prediction. Future Generation Computer Systems, 2021, 121: 25–34. DOI: 10.1016/j.future.2021.03.003.
    [27]
    Geng X, Li Y, Wang L, Zhang L, Yang Q, Ye J, Liu Y. Spatiotemporal multi-graph convolution network for ride-hailing demand forecasting. In Proc. the 33rd AAAI Conference on Artificial Intelligence, Jan. 27–Feb. 1, 2019, pp.3656–3663. DOI: 10.1609/aaai.v33i01.33013656.
    [28]
    Liu H, Han J, Fu Y, Li Y, Chen K, Xiong H. Unified route representation learning for multi-modal transportation recommendation with spatiotemporal pre-training. The VLDB Journal, 2023, 32(2): 342–350. DOI: 10.1007/s00778-022-00748-y.
    [29]
    Wang X, Sun G, Fang X, Yang J, Wang S. Modeling spatio-temporal neighbourhood for personalized point-of-interest recommendation. In Proc. the 31st International Joint Conference on Artificial Intelligence, Jul. 2022, pp.3530–3536. DOI: 10.24963/ijcai.2022/490.
    [30]
    Li M, Zhu Z. Spatial-temporal fusion graph neural networks for traffic flow forecasting. In Proc. the 35th AAAI Conference on Artificial Intelligence, Feb. 2021, pp.4189–4196. DOI: 10.1609/aaai.v35i5.16542.
    [31]
    Park H S, Jun C H. A simple and fast algorithm for K-medoids clustering. Expert Systems with Applications, 2009, 36(2): 3336–3341. DOI: 10.1016/j.eswa.2008.01.039.
    [32]
    Brereton R G, Lloyd G R. Support vector machines for classification and regression. Analyst, 2010, 135(2): 230–267. DOI: 10.1039/B918972F.
    [33]
    Wang D, Fu Y, Wang P, Huang B, Lu C T. Reimagining city configuration: Automated urban planning via adversarial learning. In Proc. the 28th International Conference on Advances in Geographic Information Systems, Nov. 2020, pp.497–506. DOI: 10.1145/3397536.3422268.
    [34]
    Tobler W. On the first law of geography: A reply. Annals of the Association of American Geographers, 2004, 94(2): 304–310. DOI: 10.1111/j.1467-8306.2004.09402009.x.
    [35]
    Haklay M, Weber P. OpenStreetMap: User-generated street maps. IEEE Pervasive Computing, 2008, 7(4): 12–18. DOI: 10.1109/MPRV.2008.80.
    [36]
    Hengl T, Heuvelink G B M, Stein A. A generic framework for spatial prediction of soil variables based on regression-kriging. Geoderma, 2004, 120(1/2): 75–93. DOI: 10.1016/j.geoderma.2003.08.018.
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