Abstract
With the rapid popularity of smart mobile devices and the rapid development of location-based social networks (LBSNs), location-based recommendation has become an important method to help people find the attractive point-of-interest (POI). However, due to the sparsity of user-POI check-in data, the traditional recommendation model based on collaborative filtering cannot be well applied to the POI recommendation problem. In addition, location-based social networks are different from other recommendation scenarios, and users’ POI check-ins are closely related to social relations and geographical factors. Therefore, this paper proposes a neural networks POI recommendation model fused with social and geographical graph embedding representation(SG-NeuRec). Our model organically combines social and geographical graph embedding representations with user-POI interaction representation, and captures the latent interactions between users and POIs under the neural networks framework. Meanwhile, in order to improve the accuracy of POI recommendation, the relevance between users’ accessing time pattern and POI is modeled by the designed shallow network and unified under the same framework. Extensive experiments on two real location-based social networks datasets demonstrate the effectiveness of the proposed model.
Supported by Chinese National Natural Science Foundation (61602159).
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
References
Cai, Z., He, Z.: Trading private range counting over big IoT data. In: The 39th IEEE International Conference on Distributed Computing Systems (ICDCS 2019) (2019)
He, X., Liao, L., Zhang, H., Nie, L., Hu, X., Chua, T.S.: Neural collaborative filtering. In: Proceedings of the 26th International Conference on World Wide Web, pp. 173–182. ACM (2017)
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: Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 831–840. ACM (2014)
Ma, H., Yang, H., Lyu, M.R., King, I.: SoRec: social recommendation using probabilistic matrix factorization. In: Proceedings of the 17th ACM Conference on Information and Knowledge Management, pp. 931–940. ACM (2008)
Qi, L., et al.: Structural balance theory-based e-commerce recommendation over big rating data. IEEE Trans. Big Data 4(3), 301–312 (2018)
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: Bayesian personalized ranking from implicit feedback. In: Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence (2009)
Wang, D., Cui, P., Zhu, W.: Structural deep network embedding. In: Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1225–1234. ACM (2016)
Wang, Y., Cai, Z., Tong, X., Gao, Y., Yin, G.: Truthful incentive mechanism with location privacy-preserving for mobile crowdsourcing systems. Comput. Netw. 135, 32–43 (2018)
Wang, Y., Cai, Z., Zhan, Z., Gong, Y., Tong, X.: An optimization and auction based incentive mechanism to maximize social welfare for mobile crowdsourcing. IEEE Trans. Comput. Soc. Syst. (2019)
Wang, Y., Yin, G., Cai, Z., Dong, Y., Dong, H.: A trust-based probabilistic recommendation model for social networks. J. Netw. Comput. Appl. 55, 59–67 (2015)
Xia, X., Yu, J., Zhang, S., et al.: Trusted service scheduling and optimization strategy design of service recommendation. Secur. Commun. Netw. 2017, 1–9 (2017)
Xie, M., Yin, H., Wang, H., Xu, F., Chen, W., Wang, S.: Learning graph-based poi embedding for location-based recommendation. In: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management, pp. 15–24. ACM (2016)
Yanwei, X., Lianyong, Q., Wanchun, D., et al.: Privacy-preserving and scalable service recommendation based on simhash in a distributed cloud environment. Complexity 2017, 1–9 (2017)
Yang, C., Bai, L., Zhang, C., Yuan, Q., Han, J.: 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, pp. 1245–1254. ACM (2017)
Yuan, Q., Cong, G., Ma, Z., Sun, A., Thalmann, N.M.: Time-aware point-of-interest recommendation. In: Proceedings of the 36th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 363–372. ACM (2013)
Zhang, J.D., Chow, C.Y.: GeoSoCa: exploiting geographical, social and categorical correlations for point-of-interest recommendations. In: Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval, pp. 443–452. ACM (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Zhu, J., Guo, X. (2019). Deep Neural Model for Point-of-Interest Recommendation Fused with Graph Embedding Representation. In: Biagioni, E., Zheng, Y., Cheng, S. (eds) Wireless Algorithms, Systems, and Applications. WASA 2019. Lecture Notes in Computer Science(), vol 11604. Springer, Cham. https://doi.org/10.1007/978-3-030-23597-0_40
Download citation
DOI: https://doi.org/10.1007/978-3-030-23597-0_40
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-23596-3
Online ISBN: 978-3-030-23597-0
eBook Packages: Computer ScienceComputer Science (R0)