Awareness of Check-In Patterns for an Adaptive Framework in Next POI Recommendation
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Abstract
The recommendation of the Next Point-of-Interest (Next POI) is pivotal in the domain of location-based services, as it forecasts a user's subsequent check-in based on their historical movement patterns. Although prior researches have recognized and acknowledged the diversity in individual travel behavior, the methodologies for effectively distinguishing these patterns remain somewhat ambiguous and unclear. This particular challenge becomes more complex when users engage in check-ins at irregular locations or times, which consequently complicates the process of forecasting the Next POI. To address this issue, we aim to analyze the check-in patterns to improve the Next POI recommendation process. To address this particular concern, we analyze check-in patterns to improve the Next POI recommendation process. We propose AFNextPOI (Awareness of Check-In Patterns for an Adaptive Framework in Next POI Recommendation), which enhances check-in pattern analysis through pattern-based features. This research implements strict privacy protection measures, utilizing only anonymized check-in data to ensure no user profile information is accessible or analyzed. Experiments conducted on two real-world datasets demonstrate that the AFNextPOI framework achieves superior performance compared to state-of-the-art models in terms of Recall and NDCG metrics, thereby validating the effectiveness of our approach.
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References
M. Rizwan, W. Wan and L. Gwiazdzinski, “Visualization, spatiotemporal patterns, and directional analysis of urban activities using geolocation data extracted from LBSN,” ISPRS International Journal of Geo-Information, vol. 9, no. 2, p. 137, 2020.
T. Qian, B. Liu, Q. V. H. Nguyen and H. Yin, “Spatiotemporal representation learning for translation-based POI recommendation,” ACM Transactions on Information Systems (TOIS), vol. 37, no. 2, pp. 1-24, 2019.
X. Wang, Y. Liu, X. Zhou, X. Wang and Z. Leng, “A point-of-interest recommendation method exploiting sequential, category and geographical influence,” ISPRS International Journal of Geo-Information, vol. 11, no. 2, p. 80, 2022.
L. Sun, “POI recommendation method based on multi-source information fusion using deep learning in location-based social networks,” Journal of Information Processing Systems, vol. 17, no. 2, pp. 352-368, 2021.
X. Liu, Y. Yang, Y. Xu, F. Yang, Q. Huang and H. Wang, “Real-time POI recommendation via modeling long-and short-term user preferences,” Neurocomputing, vol. 467, pp. 454-464, 2022.
S. M. Rahimi, B. Far and X. Wang, “Contextual location recommendation for location-based social networks by learning user intentions and contextual triggers,” GeoInformatica, vol. 26, no. 1, pp. 1-28, 2022.
X. Xiong et al., “Where to go: An effective point-of-interest recommendation framework for heterogeneous social networks,” Neurocomputing, vol. 373, pp. 56-69, 2020.
J. Ding, G. Yu, Y. Li, D. Jin and H. Gao, “Learning from hometown and current city: Crosscity POI recommendation via interest drift and transfer learning,” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies, vol. 3, no. 4, pp. 1-28, 2019.
X. Wang, X. Liu, L. Li, X. Chen, J. Liu and H. Wu, “Time-aware User Modeling with Check-in Time Prediction for Next POI Recommendation,” 2021 IEEE International Conference on Web Services (ICWS), Chicago, IL, USA, pp. 125-134, 2021.
Y. Wu, K. Li, G. Zhao and X. Qian, “Personalized Longand Short-term Preference Learning for Next POI Recommendation,” in IEEE Transactions on Knowledge and Data Engineering, vol. 34, no. 4, pp. 1944-1957, 1 April 2022.
K. Sun, T. Qian, T. Chen, Y. Liang, Q. V. H. Nguyen and H. Yin, “Where to go next: Modeling long-and short-term user preferences for point-of-interest recommendation,” in Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, no. 01, pp. 214-221, 2020.
X. Sun, B. Huang, X. Wang and D. Yu, “MARAN: Supporting awareness of users’ routines and preferences for next POI recommendation based on spatial aggregation,” Expert Systems with Applications, vol. 238, p. 121961, 2024.
H. Sun, J. Xu, R. Zhou, W. Chen, L. Zhao and C. Liu, “HOPE: a hybrid deep neural model for out-of-town next POI recommendation,” World Wide Web, vol. 24, no. 5, pp. 1749-1768, 2021.
T. Yang, Y. Gao, Z. Huang and Y. Liu, “UPTDNet: A User Preference Transfer and Drift Network for Cross-City Next POI Recommendation,” International Journal of Intelligent Systems, vol. 2023, no. 1, p. 9091570, 2023.
S. Liu and L. Wang, “A self-adaptive point-ofinterest recommendation algorithm based on a multi-order Markov model,” Future Generation Computer Systems, vol. 89, pp. 506-514, 2018.
O. Sooknit, J. Suksawatchon and U. Suksawatchon, “Next Point of Interest Recommendation Using Adaptive Weights for Specific Behavioral Patterns,” 2024 28th International Computer Science and Engineering Conference (ICSEC), Khon Kaen, Thailand, pp. 1-6, 2024.
D. Xu, Y. Wang, Y. Meng and Z. Zhang, “An Improved Data Anomaly Detection Method Based on Isolation Forest,” 2017 10th International Symposium on Computational Intelligence and Design (ISCID), Hangzhou, China, pp. 287291, 2017.
F. T. Liu, K. M. Ting and Z. -H. Zhou, “Isolation Forest,” 2008 Eighth IEEE International Conference on Data Mining, Pisa, Italy, pp. 413422, 2008.
S. Hochreiter, “Long Short-term Memory,” Neural Computation MIT-Press, 1997.
N. Panigrahi, Computing in geographic information systems. CRC Press, 2014.
K. Banerjee, R. R. Gupta, K. Vyas and B. Mishra, “Exploring alternatives to softmax function,” arXiv preprint arXiv:2011.11538, 2020.
D. Yang, D. Zhang, V. W. Zheng and Z. Yu, “Modeling User Activity Preference by Leveraging User Spatial Temporal Characteristics in LBSNs,” in IEEE Transactions on Systems, Man, and Cybernetics: Systems, vol. 45, no. 1, pp. 129142, Jan. 2015.
H. N. Bhandari, B. Rimal, N. R. Pokhrel, R. Rimal and K. R. Dahal, “LSTM-SDM: An integrated framework of LSTM implementation for sequential data modeling,” Software Impacts, vol. 14, p. 100396, 2022.
Z. Zhang, C. Li, Z. Wu, A. Sun, D. Ye and X. Luo, “Next: a neural network framework for next poi recommendation,” Frontiers of Computer Science, vol. 14, pp. 314-333, 2020.
