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A Meta-Path-Based Recurrent Model for Next POI Prediction with Spatial and Temporal Contexts

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Web and Big Data (APWeb-WAIM 2019)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 11642))

Abstract

Predicting next point of interest (POI) of users in location-based social networks has become an increasingly significant requirement, because of its potential benefits for individuals and businesses. Recently, various recurrent neural network architectures have incorporated contextual information associated with users’ sequence of check-ins to capture their dynamic preferences. However, these architectures are limited because they only take the sequential order of check-ins into account and face difficulties in remembering long-range dependencies. In this work, we resort to the heterogeneous of information network (HIN) to address these issues. Specifically, a novel attentional meta-path-based recurrent neural network is proposed, dubbed ST-HIN. ST-HIN predicts the next POI of users from their spatial–temporal incomplete historical check-in sequences, and uses the multi-modal recurrent neural network to capture the complex transition relationship. Furthermore, a meta-path attention embedding module is devised to capture the mutual influence between the users’ meta-path-based global information in HIN and the dynamic status of their current mobility. The results of extensive experiments performed on real-world datasets demonstrate the effectiveness of our proposed model.

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References

  1. Chen, M., Liu, Y., Yu, X.: NLPMM: a next location predictor with Markov modeling. In: Tseng, V.S., Ho, T.B., Zhou, Z.-H., Chen, A.L.P., Kao, H.-Y. (eds.) PAKDD 2014. LNCS (LNAI), vol. 8444, pp. 186–197. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-06605-9_16

    Chapter  Google Scholar 

  2. Cheng, C., Yang, H., King, I., Lyu, M.R.: Fused matrix factorization with geographical and social influence in location-based social networks. In: AAAI Conference on Artificial Intelligence, pp. 17–23 (2012)

    Google Scholar 

  3. Cheng, C., Yang, H., Lyu, M.R., King, I.: Where you like to go next: Successive point-of-interest recommendation. In: International Joint Conference on Artificial Intelligence, pp. 2605–2611 (2013)

    Google Scholar 

  4. Dong, X., Yu, L., Wu, Z., Sun, Y., Yuan, L., Zhang, F.: A hybrid collaborative filtering model with deep structure for recommender systems. In: AAAI Conference on Artificial Intelligence, pp. 1309–1315 (2017)

    Google Scholar 

  5. Feng, S., Li, X., Zeng, Y., Cong, G., Chee, Y.M., Yuan, Q.: Personalized ranking metric embedding for next new poi recommendation. In: International Joint Conference on Artificial Intelligence, pp. 2069–2075 (2015)

    Google Scholar 

  6. Feng, W., Wang, J.: Incorporating heterogeneous information for personalized tag recommendation in social tagging systems. In: SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1276–1284 (2012)

    Google Scholar 

  7. Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: KDD, pp. 855–864 (2016)

    Google Scholar 

  8. He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.-S.: Neural collaborative filtering. In: International World Wide Web Conference, pp. 173–182 (2017)

    Google Scholar 

  9. Hu, B., Shi, C., Zhao, W.X., Yang, T.: Local and global information fusion for top-n recommendation in heterogeneous information network. In: ACM International Conference on Information and Knowledge Management, pp. 1683–1686 (2018)

    Google Scholar 

  10. Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 8, 30–37 (2009)

    Article  Google Scholar 

  11. Li, X., Cong, G., Li, X.-L., Pham, T.-A.N., Krishnaswamy, S.: Rank-GeoFM: a ranking based geographical factorization method for point of interest recommendation. In: SIGIR Conference on Research & Development in Information Retrieval, pp. 433–442 (2015)

    Google Scholar 

  12. Lian, D., Zhao, C., Xie, X., Sun, G., Chen, E., Rui, Y.: GeoMF: joint geographical modeling and matrix factorization for point-of-interest recommendation. In: SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 831–840 (2014)

    Google Scholar 

  13. Liang, D., Altosaar, J., Charlin, L., Blei, D.M.: Factorization meets the item embedding: regularizing matrix factorization with item co-occurrence. In: ACM Conference on Recommender Systems, pp. 59–66 (2016)

    Google Scholar 

  14. Liao, D., Liu, W., Zhong, Y., Li, J., Wang, G.: Predicting activity and location with multi-task context aware recurrent neural network. In: International Joint Conference on Artificial Intelligence, pp. 3435–3441 (2018)

    Google Scholar 

  15. Liao, D., Zhong, Y., Li, J.: Location prediction through activity purpose: integrating temporal and sequential models. In: Kim, J., Shim, K., Cao, L., Lee, J.-G., Lin, X., Moon, Y.-S. (eds.) PAKDD 2017. LNCS (LNAI), vol. 10234, pp. 711–723. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-57454-7_55

    Chapter  Google Scholar 

  16. Liu, Q., Wu, S., Wang, D., Li, Z., Wang, L.: Context-aware sequential recommendation. In: IEEE International Conference on Data Mining, pp. 1053–1058 (2016)

    Google Scholar 

  17. Liu, Q., Wu, S., Wang, L., Tan, T.: Predicting the next location: a recurrent model with spatial and temporal contexts. In: AAAI Conference on Artificial Intelligence, pp. 194–200 (2016)

    Google Scholar 

  18. Mnih, A., Salakhutdinov, R.R.: Probabilistic matrix factorization. In: Annual Conference on Neural Information Processing Systems, pp. 1257–1264 (2008)

    Google Scholar 

  19. Perozzi, B., Alrfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2014)

    Google Scholar 

  20. Pham, T.-A.N., Li, X., Cong, G.: A general model for out-of-town region recommendation. In: International World Wide Web Conference, pp. 401–410 (2017)

    Google Scholar 

  21. Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: International Conference on Uncertainty in Artificial Intelligence, pp. 452–461 (2009)

    Google Scholar 

  22. Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized Markov chains for next-basket recommendation. In: International World Wide Web Conference, pp. 811–820 (2010)

    Google Scholar 

  23. Shi, C., Li, Y., Zhang, J., Sun, Y., Philip, S.Y.: A survey of heterogeneous information network analysis. IEEE Trans. Knowl. Data Eng. 29(1), 17–37 (2017)

    Article  Google Scholar 

  24. Shi, C., Zhang, Z., Luo, P., Yu, P.S., Yue, Y., Wu, B.: Semantic path based personalized recommendation on weighted heterogeneous information networks. In: ACM International on Conference on Information and Knowledge Management, pp. 453–462 (2015)

    Google Scholar 

  25. Sun, Y., Han, J., Yan, X., Yu, P.S., Wu, T.: PathSim: meta path-based top-k similarity search in heterogeneous information networks. VLDB Endow. 4(11), 992–1003 (2011)

    Google Scholar 

  26. Wang, H., Wang, N., Yeung, D.-Y.: Collaborative deep learning for recommender systems. In: SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 1235–1244 (2015)

    Google Scholar 

  27. Wang, Y., et al.: Regularity and conformity: location prediction using heterogeneous mobility data. In: SIGKDD Conference on Knowledge Discovery and Data Mining, pp. 1275–1284 (2015)

    Google Scholar 

  28. Wu, C.-Y., Ahmed, A., Beutel, A., Smola, A.J., Jing, H.: Recurrent recommender networks. In: ACM International Conference on Web Search and Data Mining, pp. 495–503 (2017)

    Google Scholar 

  29. Xingjian, S., Chen, Z., Wang, H., Yeung, D.-Y., Wong, W.-K., Woo, W.-C.: Convolutional LSTM network: a machine learning approach for precipitation nowcasting. In: Annual Conference on Neural Information Processing Systems, pp. 802–810 (2015)

    Google Scholar 

  30. Yang, D., Zhang, D., Zheng, V.W., Yu, Z.: Modeling user activity preference by leveraging user spatial temporal characteristics in LBSNs. IEEE Trans. Syst. Man Cybern. 45(1), 129–142 (2015)

    Article  Google Scholar 

  31. Yao, D., Zhang, C., Huang, J., Bi, J.: SERM: a recurrent model for next location prediction in semantic trajectories. In: ACM International Conference on Information and Knowledge Management, pp. 2411–2414 (2017)

    Google Scholar 

  32. Yu, X., et al.: Personalized entity recommendation: a heterogeneous information network approach. In: ACM International Conference on Web Search and Data Mining, pp. 283–292 (2014)

    Google Scholar 

  33. Zhang, J.-D., Chow, C.-Y.: iGSLR: personalized geo-social location recommendation: a kernel density estimation approach. In: ACM International Conference on Advances in Geographic Information Systems, pp. 334–343 (2013)

    Google Scholar 

  34. Zhao, S., Zhao, T., Yang, H., Lyu, M.R., King, I.: STELLAR: spatial-temporal latent ranking for successive point-of-interest recommendation. In: AAAI Conference on Artificial Intelligence, pp. 315–322 (2016)

    Google Scholar 

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Author information

Authors and Affiliations

  1. College of Computer Science, Nankai University, Tianjin, China

    Hengpeng Xu, Peizhi Wu, Jinmao Wei & Zhenglu Yang

  2. College of Mathematics and Statistics Science, Ludong University, Shandong, China

    Jun Wang

Authors
  1. Hengpeng Xu
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  2. Peizhi Wu
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  3. Jinmao Wei
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  4. Zhenglu Yang
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  5. Jun Wang
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Corresponding authors

Correspondence to Jinmao Wei or Zhenglu Yang .

Editor information

Editors and Affiliations

  1. University of Electronic Science and Technology of China, Chengdu, China

    Jie Shao

  2. Hong Kong Polytechnic University, Hong Kong, China

    Man Lung Yiu

  3. The University of Tokyo, Tokyo, Japan

    Masashi Toyoda

  4. Zhejiang University, Hangzhou, China

    Dongxiang Zhang

  5. National University of Singapore, Singapore, Singapore

    Wei Wang

  6. Peking University, Beijing, China

    Bin Cui

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Xu, H., Wu, P., Wei, J., Yang, Z., Wang, J. (2019). A Meta-Path-Based Recurrent Model for Next POI Prediction with Spatial and Temporal Contexts. In: Shao, J., Yiu, M., Toyoda, M., Zhang, D., Wang, W., Cui, B. (eds) Web and Big Data. APWeb-WAIM 2019. Lecture Notes in Computer Science(), vol 11642. Springer, Cham. https://doi.org/10.1007/978-3-030-26075-0_17

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  • DOI: https://doi.org/10.1007/978-3-030-26075-0_17

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  • Online ISBN: 978-3-030-26075-0

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