[1] I. Portugal, P. Alencar, and D. Cowan, “The use of machine learning algorithms in recommender systems: A systematic review,” Expert Systems with Applications, vol. 97, 2018, pp. 205-227.
[2] X. Amatriain, and J. Basilico, “Recommender systems in Industry: A Netflix case study,” Recommender Systems Handbook, Springer, 2015, pp. 385- 419.
[3] Y. Liu, T. Pham, G. Cong and Q. Yuan, “An experimental evaluation of point-of- interest recommendation in location-based social networks,” In Proceedings of the VLDB Endowment, vol. 10, no. 10, 2017, pp. 1010-1021.
[4] Z. Sun, D. Yu,H. Fang, J. Yang, X. Qu, J. Zhang and C. Geng, “Are we evaluating rigorously? benchmarking recommendation for reproducible evaluation and fair comparison,” In Proceedings of Fourteenth ACM conference on recommender systems, 2020, pp. 23-32.
[5] G. Ference, M. Ye and W. Lee, “Location recommendation for out-of-town users in location-based social networks,” In Proceedings of the 22nd ACM international conference on Information & Knowledge Management, 2013, pp. 721-726.
[6] M. Ye, P. Yin, W. Lee, and D. Lee, “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, 2011, pp. 325-334.
[7] J. Zhang and C. Chow, “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, 2015, pp. 443–452.
[8] D. Zhang, M. Li, and C. Wang, “Point of interest recommendation with social and geographical influence,” In Proceedings of 2016 IEEE international conference on big data, 2016, pp. 1070–1075.
[9] H. Wang, M. Terrovitis, and N. Mamoulis, “Location recommendation in location- based social networks using user check-in data,” in Proceedings of the 21st ACM SIGSPATIAL international conference on Advances in Geographic Information Systems, 2013, pp. 374–383.
[10] J. Zhang and C.Y. Chow, “iGSLR: personalized geo-social location recommendation: a kernel density estimation approach,” in Proceedings of the 21st ACM SIGSPATIAL International Conf. on Advances in Geographic Information Systems, 2013, pp. 334-343.
[11] H.Gao, J.Tang, X.Hu, and H.Liu, “Exploring temporal effects for location recommendation on location-based social networks,” In Proceedings of the 7th ACM conference on Recommender systems, 2013, pp. 93-100.
[12] C. Cheng, H. Yang, I. King, and M. R. Lyu, “Fused matrix factorization with geographical and social influence in location-based social networks,” In Proceedings of the Twenty-sixth Conference on Artificial Intelligence, 2012, pp. 17-23.
[13] F. Yu, L. Cui, W. Guo, X. Lu, Q. Li, and H. Lu, “A category-aware deep model for successive poi recommendation on sparse check-in data,” In Proceedings of the web conference 2020, 2020, pp. 1264-1274.
[14] P. Zhao, H. Zhu,Y.Liu, Z. Li, J. Xu, and V. Sheng, “Where to go next: A spatio-temporal LSTM model for next POI recommendation,” In Proceedings of the Thirty-Third AAAI Conference on Artificial Intelligence, 2019, pp. 5877-5884.
[15] H. Jiao, F. Mo, and H. Yamana, “Evaluation of POI recommendation system beyond accuracy: Diversity, explainability and computation cost,” In Proceedings of 18th Japan data engineering and information management (DEIM) forum, C4-4, 2020.
[16] D. Agarwal and M. Gurevich, “Fast top-k retrieval for model based recommendation,” In Proceedings of the fifth ACM international conference on Web search and data mining, 2012, pp. 484-492.
[17] X. Jiao, Y. Xiao, W. Zheng, H. Wang, and Y. Jin, “R2SIGTP: A novel real- time recommendation system with integration of geography and temporal preference for next point-of-interest,” In Proceedings of the World Wide Web conference, 2019, pp. 3560-3563.
[18] Q. Fan, L. Jiao, C. Dai, Z. Deng, and R. Zhang. “Golang-based POI discovery and recommendation in real time,” In Proceedings of the 2019 20th IEEE International Conference on Mobile Data Management (MDM), 2019, pp. 527-532.
[19] Q. Wang, T. Chen, Z. Huang, and H. Wang, “Next point-of-interest recommendation on resource-constrained mobile devices,” In Proceedings of the Web conference 2020, 2020, pp. 906–916.
[20] Y. Si, F. Zhang, and W.Liu, “CTF-ARA: An adaptive method for POI recommendation based on check-in and temporal features,” Knowledge-Based Systems, vol.128, 2017, pp.59-70.
[21] J. Zhu, C.Wang, X. Guo, Q. Ming, J. Li, and Y. Liu, “Friend and POI recommendation based on social trust cluster in location-based social networks,” EURASIP Journal on Wireless Communications and Networking, 2019, vol 2019, no. 1:89, pp. 1-12.
[22] D. Massimo and F. Ricci, “Clustering users’ POIs visit trajectories for next- POI recommendation,” Information and Communication Technologies in Tourism 2019, 2019, Springer, pp. 3-14.