Abstract
Focusing on learning the user’s behavioral characteristics during check-in activities, the next point of interest (POI) recommendation is to predict user’s destination to visit next. It is important for both the location-based service providers and users. Most of the existing studies have not taken full advantage of spatio-temporal information and user category preference, these are very important for analyzing user preference. Therefore, we propose a next POI recommendation algorithm named as CPAM that integrates category preference and attention mechanism to comprehensively structure user mobility patterns. We adopt the LSTM with multi-level attention mechanism to get user POI preference, which studies the weight of different contextual information of each check-in, and the different influence of each check-in the sequence to the next POI. In addition, we use LSTM to capture the user’s category transition preference to further improve the accuracy of recommendation. The experiment results on two real-world Foursquare datasets demonstrate that CPAM has better performance than the state-of-the art methods in terms of two commonly used metrics.
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References
Chen, W., Yue, L., Li, B., Wang, C., Sheng, Q.Z.: DAMTRNN: a delta attention-based multi-task RNN for intention recognition. In: Li, J., Qin, S., Li, X., Wang, S., Wang, S. (eds.) ADMA 2019. LNCS (LNAI), vol. 11888, pp. 373–388. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-35231-8_27
Dai, S., Yu, Y., Fan, H., Dong, J.: Spatio-temporal representation learning with social tie for personalized poi recommendation. Data Sci. Eng. 7(1), 44–56 (2022)
Huang, L., Ma, Y., Wang, S., Liu, Y.: An attention-based spatiotemporal LSTM network for next POI recommendation. IEEE Trans. Serv. Comput. 14(6), 1585–1597 (2021)
Jiao, W., Fan, H., Midtbø, T.: A grid-based approach for measuring similarities of taxi trajectories. Sensors 20(11), 3118 (2020)
Liu, Q., Wu, S., Wang, L., Tan, T.: Predicting the next location: a recurrent model with spatial and temporal contexts. In: Proceedings of the Thirtieth AAAI Conference on Artificial Intelligence, February 12–17, 2016, Phoenix, Arizona, USA, pp. 194–200. AAAI Press (2016)
Lv, Q., Qiao, Y., Ansari, N.: Big data driven hidden markov model based individual mobility prediction at points of interest. IEEE Trans. Veh. Technol. 66(6), 5204–5216 (2017)
Rendle, S., Freudenthaler, C., Gantner, Z., Schmidt-Thieme, L.: BPR: bayesian personalized ranking from implicit feedback. CoRR abs/1205.2618 (2012)
Wu, Y., Li, K., Zhao, G., Qian, X.: Long- and short-term preference learning for next POI recommendation. In: Proceedings of the 28th ACM International Conference on Information and Knowledge Management, CIKM 2019, Beijing, China, November 3–7, 2019, pp. 2301–2304. ACM (2019)
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. Syst. 45(1), 129–142 (2015)
Yu, D., Wanyan, W., Wang, D.: Leveraging contextual influence and user preferences for point-of-interest recommendation. Multimedia Tools Appl. 80(1), 1487–1501 (2021)
Zhang, L., Sun, Z., Zhang, J., Lei, Y.: An interactive multi-task learning framework for next POI recommendation with uncertain check-ins. In: Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020, pp. 3551–3557. ijcai.org (2020)
Zhao, P., Zhu, H., Liu, Y., Xu, J., Li, Z.: Where to go next: A spatio-temporal gated network for next POI recommendation. In: The Thirty-Third AAAI Conference on Artificial Intelligence, AAAI, pp. 5877–5884. AAAI Press (2019)
Zheng, C., Tao, D., Wang, J.: Memory augmented hierarchical attention network for next point-of-interest recommendation. IEEE Trans. Comput. Soc. Syst. 8(2), 489–499 (2021)
Zhu, Y., Li, H., Liao, Y., Wang, B.: What to do next: Modeling user behaviors by time-lstm. In: Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, IJCAI 2017, pp. 3602–3608. ijcai.org (2017)
Acknowledgements
This work is supported by the National Nature Science Foundation of China (62172186) and the Fundamental Research Funds for the Central Universities, JLU under Grant No.93K172021Z02.
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Wang, X., Liu, Y., Zhou, X., Leng, Z., Wang, X. (2023). Next POI Recommendation Method Based on Category Preference and Attention Mechanism in LBSNs. In: Li, B., Yue, L., Tao, C., Han, X., Calvanese, D., Amagasa, T. (eds) Web and Big Data. APWeb-WAIM 2022. Lecture Notes in Computer Science, vol 13422. Springer, Cham. https://doi.org/10.1007/978-3-031-25198-6_2
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DOI: https://doi.org/10.1007/978-3-031-25198-6_2
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