Abstract
Next Point-of-Interest (POI) recommendation, which aims to recommend next POIs that the user will likely visit in the near future, has become essential in Location-based Social Networks (LBSNs). Various Recurrent Neural Network (RNN) based sequential models have been proposed for next POI recommendation and achieved state-of-the-art performance, however RNN is difficult to parallelize which limits its efficiency. Recently, Self-Attention Network (SAN), which is purely based on the self-attention mechanism instead of recurrent modules, improves both performance and efficiency in various sequential tasks. However, none of the existing self-attention networks consider the spatio-temporal intervals between neighbor check-ins, which are essential for modeling user check-in behaviors in next POI recommendation. To this end, in this paper, we propose a new Spatio-Temporal Self-Attention Network (STSAN), which combines self-attention mechanisms with spatio-temporal patterns of users’ check-in history. Specifically, time-specific weight matrices and distance-specific weight matrices through a decay function are used to model the spatio-temporal influence of POI pairs. Moreover, we introduce a simple but effective way to dynamically measure the importances of spatial and temporal weights to capture users’ spatio-temporal preferences. Finally, we evaluate the proposed model using two real-world LBSN datasets, and the experimental results show that our model significantly outperforms the state-of-the-art approaches for next POI recommendation.
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
Cheng, C., Yang, H., King, I., Lyu, M.R.: Fused matrix factorization with geographical and social influence in location-based social networks. In: AAAI (2012)
Cheng, C., Yang, H., Lyu, M.R., King, I.: Where you like to go next: successive point-of-interest recommendation. In: IJCAI, pp. 2605–2611 (2013)
Cho, E., Myers, S.A., Leskovec, J.: Friendship and mobility: user movement in location-based social networks. In: SIGKDD, pp. 1082–1090. ACM (2011)
Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555 (2014)
Feng, S., Li, X., Zeng, Y., Cong, G., Chee, Y.M., Yuan, Q.: Personalized ranking metric embedding for next new POI recommendation. In: IJCAI, pp. 2069–2075 (2015)
He, J., Li, X., Liao, L., Song, D., Cheung, W.K.: Inferring a personalized next point-of-interest recommendation model with latent behavior patterns. In: AAAI, pp. 137–143 (2016)
He, R., McAuley, J.: Fusing similarity models with Markov chains for sparse sequential recommendation. In: ICDM, pp. 191–200. IEEE (2016)
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Kang, W.C., McAuley, J.: Self-attentive sequential recommendation. In: ICDM, pp. 197–206. IEEE (2018)
Kong, D., Wu, F.: HST-LSTM: a hierarchical spatial-temporal long-short term memory network for location prediction. In: IJCAI, pp. 2341–2347 (2018)
Koren, Y., Bell, R.M., Volinsky, C.: Matrix factorization techniques for recommender systems. IEEE Comput. 42(8), 30–37 (2009)
Lei Ba, J., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv preprint arXiv:1607.06450 (2016)
Liu, Q., Wu, S., Wang, L., Tan, T.: Predicting the next location: a recurrent model with spatial and temporal contexts. In: AAAI, pp. 194–200 (2016)
Rendle, S., Freudenthaler, C., Schmidt-Thieme, L.: Factorizing personalized Markov chains for next-basket recommendation. In: WWW, pp. 811–820. ACM (2010)
Tan, Z., Wang, M., Xie, J., Chen, Y., Shi, X.: Deep semantic role labeling with self-attention. In: AAAI, pp. 4929–4936 (2018)
Vaswani, A., et al.: Attention is all you need. In: NIPS, pp. 5998–6008 (2017)
Xu, C., et al.: Graph contextualized self-attention network for session-based recommendation. In: Kraus, S. (ed.) IJCAI, pp. 3940–3946 (2019)
Yang, C., Bai, L., Zhang, C., Yuan, Q., Han, J.: Bridging collaborative filtering and semi-supervised learning: a neural approach for poi recommendation. In: SIGKDD, pp. 1245–1254. ACM (2017)
Yang, D., Zhang, D., Zheng, V.W., Yu, Z.: Modeling user activity preference by leveraging user spatial temporal characteristics in LBSNs. IEEE Trans. 45(1), 129–142 (2015)
Yao, L., Sheng, Q.Z., Qin, Y., Wang, X., Shemshadi, A., He, Q.: Context-aware point-of-interest recommendation using tensor factorization with social regularization. In: SIGIR, pp. 1007–1010. ACM (2015)
Yu, F., Liu, Q., Wu, S., Wang, L., Tan, T.: A dynamic recurrent model for next basket recommendation. In: SIGIR, pp. 729–732. ACM (2016)
Zhang, T., et al.: Feature-level deeper self-attention network for sequential recommendation. In: Kraus, S. (ed.) IJCAI, pp. 4320–4326 (2019)
Zhang, Z., Li, C., Wu, Z., Sun, A., Ye, D., Luo, X.: Next: a neural network framework for next POI recommendation. arXiv preprint arXiv:1704.04576 (2017)
Zhao, P., et al.: Where to go next: a spatio-temporal gated network for next POI recommendation. In: AAAI, pp. 5877–5884 (2019)
Zhou, C., et al.: Atrank: an attention-based user behavior modeling framework for recommendation. In: AAAI, pp. 4564–4571 (2018)
Zhu, Y., et al.: What to do next: modeling user behaviors by time-LSTM. In: IJCAI, pp. 3602–3608 (2017)
Acknowledgements
This research was partially supported by NSFC (No. 61876117, 61876217, 61872258, 61728205), Open Program of Key Lab of IIP of CAS (No. IIP2019-1) and PAPD.
Author information
Authors and Affiliations
Corresponding authors
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2020 Springer Nature Switzerland AG
About this paper
Cite this paper
Ni, J. et al. (2020). Spatio-Temporal Self-Attention Network for Next POI Recommendation. In: Wang, X., Zhang, R., Lee, YK., Sun, L., Moon, YS. (eds) Web and Big Data. APWeb-WAIM 2020. Lecture Notes in Computer Science(), vol 12317. Springer, Cham. https://doi.org/10.1007/978-3-030-60259-8_30
Download citation
DOI: https://doi.org/10.1007/978-3-030-60259-8_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-60258-1
Online ISBN: 978-3-030-60259-8
eBook Packages: Computer ScienceComputer Science (R0)