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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
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
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)
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)
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)
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)
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)
Grover, A., Leskovec, J.: node2vec: scalable feature learning for networks. In: KDD, pp. 855–864 (2016)
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)
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)
Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 8, 30–37 (2009)
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)
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)
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)
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)
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
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)
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)
Mnih, A., Salakhutdinov, R.R.: Probabilistic matrix factorization. In: Annual Conference on Neural Information Processing Systems, pp. 1257–1264 (2008)
Perozzi, B., Alrfou, R., Skiena, S.: DeepWalk: online learning of social representations. In: ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (2014)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
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)
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
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
Download citation
DOI: https://doi.org/10.1007/978-3-030-26075-0_17
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-26074-3
Online ISBN: 978-3-030-26075-0
eBook Packages: Computer ScienceComputer Science (R0)