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Global spatio-temporal aware graph neural network for next point-of-interest recommendation

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Abstract

Next Point-of-Interest (POI) recommendation is becoming increasingly popular with the rapidly growing of Location-based Social Networks (LBSNs). Most existing models only focus on exploring the local spatio-temporal relationships between POIs based on the trajectory sequence of current user. However, we argue that there exits not only local spatio-temporal relationship but also global spatio-temporal relationship, where two POIs are correlated if they appear in the trajectories of all users within certain geographical distance and time intervals. In this paper, we propose Global Spatio-Temporal Aware Graph Neural Network (GSTA-GNN), a model that captures and utilizes the global spatio-temporal relationships from the global view across the trajectories of all users. Specifically, we first break down the independence between trajectories and link all pairs of POIs based on the POI transitions to construct a global spatial graph and a global temporal graph. Then graph neural network is utilized to learn the global general representations of POIs. In addition, we introduce the spatio-temporal weight matrix, which converts the spatial and temporal intervals into suitable weight values and combines them in an adaptive manner. Then we propose to incorporate the spatio-temporal weight matrix into self-attention module of a multi-head self-attention layer to enrich the personalized representation of user trajectory. Experiments on three real datasets show that GSTA-GNN is superior to the state-of-the-art models in next POI recommendation task.

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References

  1. Shi H, Chen L, Xu Z, Lyu D (2019) Personalized location recommendation using mobile phone usage information. Appl Intell 49(10):3694–3707

    Article  Google Scholar 

  2. Han P, Shang S, Sun A, Zhao P, Zheng K, Zhang X (2021) Point-of-interest recommendation with global and local context. IEEE Trans Knowl Data Eng 1–1

  3. Zhao P, Zhu H, Liu Y, Xu J, Li Z, Zhuang F, Sheng VS, Zhou X (2019) Where to go next: a spatio-temporal gated network for next poi recommendation. In: Proceedings of the AAAI conference on artificial intelligence (AAAI), pp 5877–5884

  4. Zhao K, Zhang Y, Yin H, Wang J, Zheng K, Zhou X, Xing C (2020) Discovering subsequence patterns for next poi recommendation. In: Proceedings of the 29th international joint conference on artificial intelligence(IJCAI), pp 3216–3222

  5. Chang L, Chen W, Huang J, Bin C, Wang W (2021) Exploiting multi-attention network with contextual influence for point-of-interest recommendation. Appl Intell 51(4):1904–1917

    Article  Google Scholar 

  6. Xing S, Liu F, Wang Q, Zhao X, Li T (2019) Content-aware point-of-interest recommendation based on convolutional neural network. Appl Intell 49(3):858–871

    Article  Google Scholar 

  7. Zhang F, Yuan NJ, Zheng K, Lian D, Xie X, Rui Y (2016) Exploiting dining preference for restaurant recommendation. In: Proceedings of the 25th international conference on World Wide Web (WWW), pp 725–735

  8. Lim KH, Chan J, Karunasekera S, Leckie C (2017) Personalized itinerary recommendation with queuing time awareness. In: Proceedings of the 40th International ACM SIGIR conference on research and development in information retrieval (SIGIR), pp 325–334

  9. Chen L, Zhang L, Cao S, Wu Z, Cao J (2020) Personalized itinerary recommendation: Deep and collaborative learning with textual information. Expert Syst Appl 144:113070

    Article  Google Scholar 

  10. Cheng C, Yang H, Lyu MR, King I (2013) Where you like to go next: successive point-of-interest recommendation. In: Proceedings of the 23th international joint conference on articial intelligence (IJCAI), pp 2605–2611

  11. Liu Q, Wu S, Wang L, Tan T (2016) Predicting the next location: a recurrent model with spatial and temporal contexts. In: Proceedings of the AAAI conference on artificial intelligence (AAAI), pp 194–200

  12. Feng J, Li Y, Zhang C, Sun F, Meng F, Guo A, Jin D (2018) Deepmove: predicting human mobility with attentional recurrent networks. In: Proceedings of the 2018 World Wide Web Conference (WWW), pp 1459–1468

  13. Zhu Y, Li H, Liao Y, Wang B, Guan Z, Liu H, Cai D (2017) What to do next: modeling user behaviors by time-lstm. In: Proceedings of the twenty-sixth international joint conference on artificial intelligence (IJCAI), vol 17, pp 3602–3608

  14. Sun K, Qian T, Chen T, Liang Y, Nguyen QVH, Yin H (2020) Where to go next: modeling long-and short-term user preferences for point-of-interest recommendation. In: Proceedings of the AAAI conference on artificial intelligence (AAAI), vol 34, pp 214–221

  15. Hamilton W, Ying Z, Leskovec J (2017) Inductive representation learning on large graphs. Advances in Neural Information Processing Systems (NIPS) 30:1025–1035

    Google Scholar 

  16. Veličković P, Cucurull G, Casanova A, Romero A., Liò P, Bengio Y (2017) Graph attention networks. 6th International conference on learning representations (ICLR) 1–12

  17. Chang B, Jang G, Kim S, Kang J (2020) Learning graph-based geographical latent representation for point-of-interest recommendation. In: Proceedings of the 29th ACM international conference on information and knowledge management (CIKM), pp 135–144

  18. Xie M, Yin H, Wang H, Xu F, Chen W, Wang S (2016) Learning graph-based poi embedding for location-based recommendation. In: Proceedings of the 25th ACM international conference on information and knowledge management (CIKM), pp 15–24

  19. Zhang J, Liu X, Zhou X, Chu X (2021) Leveraging graph neural networks for point-of-interest recommendations. Neurocomputing 462:1–13

    Article  Google Scholar 

  20. Kang W. -C., McAuley J (2018) Self-attentive sequential recommendation. In: 2018 IEEE International conference on data mining (ICDM), pp 197–206

  21. Lian D, Wu Y, Ge Y, Xie X, Chen E (2020) Geography-aware sequential location recommendation. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery and data mining (KDD), pp 2009–2019

  22. Liu T, Liao J, Wu Z, Wang Y, Wang J (2020) Exploiting geographical-temporal awareness attention for next point-of-interest recommendation. Neurocomputing 400:227–237

    Article  Google Scholar 

  23. Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser ł, Polosukhin I (2017) Attention is all you need. In: Advances in neural information processing systems (NIPS), pp 5998–6008

  24. Wang H, Shen H, Ouyang W, Cheng X (2018) Exploiting poi-specific geographical influence for point-of-interest recommendation. In: Proceedings of the twenty-seventh international joint conference on artificial intelligence (IJCAI), pp 3877–3883

  25. Yuan Z, Liu H, Liu Y, Zhang D, Yi F, Zhu N, Xiong H (2020) Spatio-temporal dual graph attention network for query-poi matching. In: Proceedings of the 43rd International ACM SIGIR conference on research and development in information retrieval (SIGIR), pp 629–638

  26. Liu Y, Yang Z, Li T, Wu D (2022) A novel poi recommendation model based on joint spatiotemporal effects and four-way interaction. Appl Intell 52(5):5310–5324

    Article  Google Scholar 

  27. Kipf TN, Welling M (2017) Semi-supervised classification with graph convolutional networks. International Conference on Learning Representations (ICLR) 1–14

  28. Yu F, Cui L, Guo W, Lu X, Li Q, Lu H (2020) A category-aware deep model for successive poi recommendation on sparse check-in data. In: Proceedings of the 2020 World Wide Web Conference (WWW), pp 1264–1274

  29. Liu Y, Liu C, Lu X, Teng M, Zhu H, Xiong H (2017) Point-of-interest demand modeling with human mobility patterns. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining (KDD), pp 947–955

  30. Luo Y, Liu Q, Liu Z (2021) Stan: spatio-temporal attention network for next location recommendation. In: Proceedings of the 2021 World Wide Web Conference (WWW), pp 2177–2185

  31. He X, Liao L, Zhang H, Nie L, Hu X, Chua T-S (2017) Neural collaborative filtering. In: Proceedings of the 26th international conference on World Wide Web (WWW), pp 173–182

  32. Yu D, Wanyan W, Wang D (2021) Leveraging contextual influence and user preferences for point-of-interest recommendation. Multimed Tools Appl 80(1):1487–1501

    Article  Google Scholar 

  33. Zang H, Han D, Li X, Wan Z, Wang M (2022) Cha: categorical hierarchy-based attention for next poi recommendation. ACM Transactions on Information Systems (TOIS) 40(1):1–22

    Article  Google Scholar 

  34. Zhou F, Yin R, Zhang K, Trajcevski G, Zhong T, Wu J (2019) Adversarial point-of-interest recommendation. In: The world wide web conference (WWW), pp 3462–34618

  35. Feng S, Li X, Zeng Y, Cong G, Chee YM, Yuan Q (2015) Personalized ranking metric embedding for next new poi recommendation. In: Twenty-fourth international joint conference on artificial intelligence (IJCAI)

  36. Yao L, Sheng QZ, Qin Y, Wang X, Shemshadi A, He Q (2015) Context-aware point-of-interest recommendation using tensor factorization with social regularization. In: Proceedings of the 38th International ACM SIGIR conference on research and development in information retrieval (SIGIR), pp 1007–1010

  37. Ye M, Yin P, Lee W-C (2010) Location recommendation for location-based social networks. In: Proceedings of the 18th SIGSPATIAL international conference on advances in geographic information systems, pp 458–461

  38. Wen P, Yuan W, Qin Q, Sang S, Zhang Z (2021) Neural attention model for recommendation based on factorization machines. Appl Intell 51(4):1829–1844

    Article  Google Scholar 

  39. Ma M, Na S, Wang H, Chen C, Xu J (2022) The graph-based behavior-aware recommendation for interactive news. Appl Intell 52(2):1913–1929

    Article  Google Scholar 

  40. Islam MA, Mohammad MM, Das SSS, Ali ME (2022) A survey on deep learning based point-of-interest (poi) recommendations. Neurocomputing 472:306–325

    Article  Google Scholar 

  41. Yang D, Fankhauser B, Rosso P, Cudre-Mauroux P (2020) Location prediction over sparse user mobility traces using rnns: Flashback in hidden states!. In: Proceedings of the twenty-ninth international joint conference on artificial intelligence (IJCAI), pp 2184–2190

  42. He J, Qi J, Ramamohanarao K (2020) Timesan: a time-modulated self-attentive network for next point-of-interest recommendation. In: 2020 International joint conference on neural networks (IJCNN), pp 1–8

  43. Zhang Y, Fu Y, Wang P, Li X, Zheng Y (2019) Unifying inter-region autocorrelation and intra-region structures for spatial embedding via collective adversarial learning. In: Proceedings of the 25th ACM SIGKDD international conference on knowledge discovery and data mining (KDD), pp 1700–1708

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Acknowledgements

This work is supported by the National Natural Science Foundation of China under Project No. 61977013. We would like to thank Assistant Professor Jingcao Yu in School of Foreign Languages, University of Electronic Science and Technology of China, for her help in proofreading this paper, by which the language of this paper has been improved significantly.

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  1. School of Computer Science and Engineering, University of Electronic Science and Technology of China, Chengdu, 611731, China

    Jingkuan Wang, Bo Yang & Haodong Liu

  2. School of Computer Science, Fudan University, Shanghai, 200441, China

    Dongsheng Li

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  1. Jingkuan Wang
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  2. Bo Yang
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  3. Haodong Liu
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  4. Dongsheng Li
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Correspondence to Bo Yang.

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Wang, J., Yang, B., Liu, H. et al. Global spatio-temporal aware graph neural network for next point-of-interest recommendation. Appl Intell 53, 16762–16775 (2023). https://doi.org/10.1007/s10489-022-04377-4

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