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Where Have You Gone: Category-aware Multigraph Embedding for Missing Point-of-Interest Identification

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Abstract

The prevalence of location-based social networks (LBSNs) provides an opportunity for human mobility behavior understanding and prediction. However, data quality issues (e.g., historical check-in POI missing, data sparsity) always limit the effectiveness of existing LBSN-oriented studies, e.g., next Point-of-Interest (POI) recommendation or prediction. In contrast to previous efforts in the above study, we focus on identifying missing POIs that the user has visited at a past specific time and develop a category-aware multigraph embedding (CAME) model. Specifically, CAME jointly captures temporal cyclic effect, user preference, and sequential transition pattern in a unified way by embedding five relational information graphs into a shared dimensional space from both POI- and category-instance levels. The proposed model also incorporates region-level spatial proximity to explore the geographical influence and derives the ranking score list of candidates for missing POI identification. Extensive experiments against state-of-the-art methods are conducted on two real datasets, and the experimental results show its superiority over other competitors. Significantly, the proposed model can be naturally extended to next POI recommendation and prediction tasks with competitive performances.

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Notes

  1. It is the L2 distance between GPS coordinates.

  2. https://sites.google.com/site/yangdingqi/home/foursquare-dataset

  3. https://github.com/Shzuwu/miss-POI-idetification.

References

  1. Cai L, Xu J, Liu J, Pei T (2018) Integrating spatial and temporal contexts into a factorization model for poi recommendation. Int J Geogr Inf Sci 32(3):524–546

    Article  Google Scholar 

  2. Chen M, Liu Y, Yu X (2014) Nlpmm: A next location predictor with markov modeling. In: Pacific-Asia Conference on Knowledge Discovery and Data Mining, 186–197. Springer

  3. Cheng C, Yang H, Lyu MR, King I (2013) Where you like to go next: Successive point-of-interest recommendation. In: Twenty-Third international joint conference on Artificial Intelligence, 2605–2611

  4. Cho K, Van Merriënboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, Bengio Y (2014) Learning phrase representations using rnn encoder-decoder for statistical machine translation. arXiv preprint arXiv:1406.1078

  5. Dong Y, Chawla NV, Swami A (2017) metapath2vec: Scalable representation learning for heterogeneous networks. In: Proceedings of the 23rd ACM SIGKDD international conference on knowledge discovery and data mining, 135–144

  6. Feng S, Li X, Zeng Y, Cong G, Chee YM (2015) Personalized ranking metric embedding for next new poi recommendation. In: IJCAI’15 Proceedings of the 24th International Conference on Artificial Intelligence, 2069–2075

  7. Feng S, Tran LV, Cong G, Chen L, Li J, Li F (2020) Hme: A hyperbolic metric embedding approach for next-poi recommendation. In: Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 1429–1438

  8. Gai K, Wu Y, Zhu L, Xu L, Zhang Y (2019) Permissioned blockchain and edge computing empowered privacy-preserving smart grid networks. IEEE Internet Things J 6(5):7992–8004

    Article  Google Scholar 

  9. Grover A, Leskovec J (2016) node2vec: Scalable feature learning for networks. In: Proceedings of the 22nd ACM SIGKDD international conference on Knowledge discovery and data mining, 855–864

  10. Hang M, Pytlarz I, Neville J (2018) Exploring student check-in behavior for improved point-of-interest prediction. In: Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 321–330

  11. Islam M, Mohammad MM, Das SSS, Ali ME et al (2020) A survey on deep learning based point-of-interest (poi) recommendations. arXiv preprint arXiv:2011.10187

  12. Kojaku S, Yoon J, Constantino I, Ahn YY (2021) Residual2vec: Debiasing graph embedding with random graphs. Adv Neural Inf Process Syst 34:24150–24163

    Google Scholar 

  13. Li H, Deng K, Cui J, Dong Z, Ma J, Huang J (2018) Hidden community identification in location-based social network via probabilistic venue sequences. Inf Sci 422:188–203

    Article  Google Scholar 

  14. Li H, Ge Y, Hong R, Zhu H (2016) Point-of-interest recommendations: Learning potential check-ins from friends. In: Proceedings of the 22nd ACM SIGKDD international conference on knowledge discovery and data mining, 975–984

  15. Li L, Zhao K, Sun R, Cai S, Liu Y (2021) Research for an adaptive classifier based on dynamic graph learning. Neural Processing Letters 1–19

  16. Li R, Shen Y, Zhu Y (2018) Next point-of-interest recommendation with temporal and multi-level context attention. In: 2018 IEEE International Conference on Data Mining (ICDM), 1110–1115

  17. Li X, Cong G, Li XL, Pham TAN, Krishnaswamy, S (2015) Rank-geofm: A ranking based geographical factorization method for point of interest recommendation. In: Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval, 433–442

  18. Li X, Han D, He J, Liao L, Wang M (2019) Next and next new poi recommendation via latent behavior pattern inference. ACM Trans Inform Syst (TOIS) 37(4):1–28

    Article  Google Scholar 

  19. Lian D, Zhao C, Xie X, Sun G, Chen E, Rui Y (2014) Geomf: joint geographical modeling and matrix factorization for point-of-interest recommendation. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, 831–840

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

  21. Liu X, Liu Y, Aberer K, Miao C (2013) Personalized point-of-interest recommendation by mining users’ preference transition. In: Proceedings of the 22nd ACM international conference on Information & Knowledge Management, 733–738

  22. Mikolov T, Sutskever I, Chen K, Corrado GS, Dean J Distributed representations of words and phrases and their compositionality. Advances in neural information processing systems 3111–3119

  23. Perozzi B, Al-Rfou R, Skiena S (2014) Deepwalk: Online learning of social representations. In: Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, 701–710

  24. Qiu M, Chen Z, Liu M (2014) Low-power low-latency data allocation for hybrid scratch-pad memory. IEEE Embed Syst Lett 6(4):69–72

    Article  Google Scholar 

  25. Rendle S, Freudenthaler C, Schmidt-Thieme L (2010) Factorizing personalized markov chains for next-basket recommendation. In: Proceedings of the 19th international conference on World wide web, 811–820

  26. Shi J, Jiang Z, Feng H (2014) Adaptive graph embedding discriminant projections. Neural Process Lett 40(3):211–226

    Article  Google Scholar 

  27. 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, 214–221

  28. Tang J, Qu M, Wang M, Zhang M, Yan J, Mei Q (2015) Line: Large-scale information network embedding. In: Proceedings of the 24th international conference on world wide web, 1067–1077

  29. Xi D, Zhuang F, Liu Y, Gu J, Xiong H, He Q (2019) Modelling of bi-directional spatio-temporal dependence and users’ dynamic preferences for missing poi check-in identification. In: Proceedings of the AAAI Conference on Artificial Intelligence, 5458–5465

  30. 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 on Conference on Information and Knowledge Management, 15–24

  31. Xu S, Fu X, Cao J, Liu B, Wang Z (2020) Survey on user location prediction based on geo-social networking data. World Wide Web 23(3):1621–1664

    Article  Google Scholar 

  32. Yang C, Bai L, Zhang C, Yuan Q, Han J (2017) Bridging collaborative filtering and semi-supervised learning: a neural approach for poi recommendation. In: Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 1245–1254

  33. 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-20, 2184–2190

  34. Yang D, Zhang D, Zheng VW, Yu Z (2014) Modeling user activity preference by leveraging user spatial temporal characteristics in lbsns. IEEE Trans Syst Man Cybernet Syst 45(1):129–142

    Article  Google Scholar 

  35. Ye M, Yin P, Lee WC, Lee DL (2011) Exploiting geographical influence for collaborative point-of-interest recommendation. In: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, 325–334

  36. Zhang L, Sun Z, Zhang J, Kloeden H, Klanner F (2020) Modeling hierarchical category transition for next poi recommendation with uncertain check-ins. Inf Sci 515:169–190

    Article  Google Scholar 

  37. 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 Twenty-Ninth international joint conference on artificial intelligence, 3216–3222

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Acknowledgements

We first gratefully acknowledge anonymous reviewers who read this draft and make any helpful suggestions. The work is supported by the National Nature Science Foundation of China (No. U1803262, U1736206, 61701194), National Social Science Foundation of China (No.19ZDA113), and the Application Foundation Frontier Project of Wuhan Science and Technology Bureau (No. 2020010601012288).

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Authors and Affiliations

  1. National Engineering Research Center for Multimedia Software, School of Computer Science, Wuhan University, Wuhan, China

    Junhang Wu, Ruimin Hu, Yilin Xiao, Lingfei Ren & Wenyi Hu

  2. School of Artificial Intelligence, Jianghan University, Wuhan, China

    Dengshi Li

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  1. Junhang Wu
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  2. Ruimin Hu
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  3. Dengshi Li
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  4. Yilin Xiao
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Correspondence to Ruimin Hu.

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Wu, J., Hu, R., Li, D. et al. Where Have You Gone: Category-aware Multigraph Embedding for Missing Point-of-Interest Identification. Neural Process Lett 55, 3025–3044 (2023). https://doi.org/10.1007/s11063-022-10996-2

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