What Your Next Check-in Might Look Like: Next Check-in Behavior Prediction
Open accessAbstract
In recent years, the next-POI recommendation has become a trending research topic in the field of trajectory data mining. For protection of user privacy, users’ complete GPS trajectories are difficult to obtain. The check-in information posted by users on social networks has become an important data source for Spatio-temporal Trajectory research. However, state-of-the-art methods neglect the social meaning and the information dissemination function of check-in behavior. The social meaning is an important reason why users are willing to post check-in on social networks, and the information dissemination function means, users can affect each other’s behavior by check-ins. The above characteristics of the check-in behavior make it different from the visiting behavior. We consider a new problem of predicting the next check-in behavior including the check-in time, the POI (point-of-interest) where the check-in is located, functional semantics of the POI, and so on. To solve the proposed problem, we build a multi-task learning model called DPMTM, and a pre-training module is designed to extract dynamic social semantics of check-in behaviors. Our results show that the DPMTM model works well in the check-in behavior problem.
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Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318 · see citing works
Deep Contextualized Word Representations
Peters, M., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1202 · Cited by 11,983 · see citing works
Cho, E., Myers, S. A., & Leskovec, J. (2011) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2020408.2020579 · Cited by 2,910 · see citing works
Factorizing personalized Markov chains for next-basket recommendation
Rendle, S., Freudenthaler, C., & Schmidt-Thieme, L. (2010) · International World Wide Web Conference (WWW) · doi:10.1145/1772690.1772773 · Cited by 2,766 · see citing works
Zheng, Y. (2015) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/2743025 · Cited by 1,667 · see citing works
Modeling Task Relationships in Multi-task Learning with Multi-gate Mixture-of-Experts
Ma, J., Zhao, Z., Yi, X., Chen, J., Hong, L., & Ed, H. (2018) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3219819.3220007 · Cited by 1,329 · see citing works
Exploiting geographical influence for collaborative point-of-interest recommendation
Ye, M., Yin, P., Lee, W.-C., & Lee, D. L. (2011) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2009916.2009962 · Cited by 1,124 · see citing works
Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts
Liu, Q., Wu, S., Wang, L., & Tan, T. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.9971 · Cited by 1,015 · see citing works
Deep Learning for Spatio-Temporal Data Mining: A Survey
Wang, S., Cao, J., & Yu, P. S. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.3025580 · Cited by 862 · see citing works
Modeling User Activity Preference by Leveraging User Spatial Temporal Characteristics in LBSNs
Yang, D., Zhang, D., Zheng, V. W., & Yu, Z. (2014) · IEEE Transactions on Systems Man and Cybernetics Systems · doi:10.1109/tsmc.2014.2327053 · Cited by 857 · see citing works
Time-aware point-of-interest recommendation
Yuan, Q., Cong, G., Ma, Z., et al. (2013) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2484028.2484030 · Cited by 761 · see citing works
Sun, K., Qian, T., Chen, T., et al. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i01.5353 · Cited by 406 · see citing works
Learning travel recommendations from user-generated GPS traces
Zheng, Y., & Xie, X. (2011) · ACM Transactions on Intelligent Systems and Technology · doi:10.1145/1889681.1889683 · Cited by 398 · see citing works
Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation
Zhao, P., Luo, A., Liu, Y., Xu, J., Li, Z., Zhuang, F., Sheng, V. S., & Zhou, X. (2020) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2020.3007194 · Cited by 395 · see citing works
Ma, X., Zhao, L., Huang, G., et al. (2018) · doi:10.1145/3209978.3210104 · Cited by 388 · see citing works
Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks
Zhang, Y., Dai, H., Xu, C., Feng, J., Wang, T., Bian, J., Wang, B., & Liu, T. (2014) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v28i1.8917 · Cited by 348 · see citing works
Li, X., Cong, G., Li, X., Pham, T.-A. N., & Krishnaswamy, S. P. (2015) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/2766462.2767722 · Cited by 340 · see citing works
Mining User Mobility Features for Next Place Prediction in Location-Based Services
Noulas, A., Scellato, S., Lathia, N., & Cecilia Mascolo, C. (2012) · Proceedings · doi:10.1109/icdm.2012.113 · Cited by 336 · see citing works
Mining significant semantic locations from GPS data
Cao, X., Cong, G., & Jensen, C. S. (2010) · Proceedings of the VLDB Endowment · doi:10.14778/1920841.1920968 · Cited by 330 · see citing works
Zhang, J.-D., Chow, C.-Y., & Li, Y. (2014) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/2666310.2666400 · Cited by 253 · see citing works
STELLAR: Spatial-Temporal Latent Ranking for Successive Point-of-Interest Recommendation
Zhao, S., Zhao, T., Yang, H., et al. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.9986 · Cited by 243 · see citing works
Point-of-Interest Recommendations
Li, H., Ge, Y., Hong, R., & Zhu, H. (2016) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/2939672.2939767 · Cited by 242 · see citing works
What's Your Next Move: User Activity Prediction in Location-based Social Networks
Ye, J., Zhu, Z., & Cheng, H. (2013) · SIAM International Conference on Data Mining · doi:10.1137/1.9781611972832.19 · Cited by 222 · see citing works
Inferring a Personalized Next Point-of-Interest Recommendation Model with Latent Behavior Patterns
He, J., Li, X., Liao, L., et al. (2016) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v30i1.9994 · Cited by 169 · see citing works
An attention‐based category‐aware GRU model for the next POI recommendation
Liu, Y., Pei, A., Wang, F., Yang, Y., Zhang, X., Wang, H., Dai, H., Qi, L., & Ma, R. (2021) · International Journal of Intelligent Systems · doi:10.1002/int.22412 · Cited by 136 · see citing works
Discovering Subsequence Patterns for Next POI Recommendation
Zhao, K., Zhang, Y., Yin, H., Wang, J., Zheng, K., Zhou, X., & Xing, C. (2020) · International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2020/445 · Cited by 127 · see citing works
Hierarchical Multi-Task Graph Recurrent Network for Next POI Recommendation
Lim, N., Hooi, B., Ng, S.-K., et al. (2022) · International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR) · doi:10.1145/3477495.3531989 · Cited by 87 · see citing works
A dummy-based anonymization method based on user trajectory with pauses
Kato, R., Iwata, M., Hara, T., et al. (2012) · ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems (SIGSPATIAL GIS) · doi:10.1145/2424321.2424354 · Cited by 81 · see citing works
Curriculum Meta-Learning for Next POI Recommendation
Chen, Y., Wang, X., Fan, M., et al. (2021) · doi:10.1145/3447548.3467132 · Cited by 75 · see citing works
Spatial-Temporal Interval Aware Sequential POI Recommendation
Wang, E., Jiang, Y., Xu, Y., Wang, L., & Yang, Y. (2022) · IEEE International Conference on Data Engineering (ICDE) · doi:10.1109/icde53745.2022.00202 · Cited by 50 · see citing works
Flagged first, then by citations
Research works printed but not linked: 12
- Chen Cheng, Haiqin Yang, Irwin King, and Michael Lyu. 2012. Fused matrix factorization with geographical and social influence in location-based social networks. In Proceedings of the AAAI Conference on Artificial Intelligence. 17-23.
- Chen Cheng, Haiqin Yang, Michael R. Lyu, and Irwin King. 2013. Where you like to go next: Successive point-of- interest recommendation. In Proceedings of the 23rd International Joint Conference on Artificial Intelligence.
- Kyunghyun Cho, Bart van Merriënboer, Caglar Gulcehre, Dzmitry Bahdanau, Fethi Bougares, Holger Schwenk, and Yoshua Bengio. 2014. Learning phrase representations using RNN encoder -decoder for statistical machine translation. In Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP'14). 1724-1734.
- Shanshan Feng, Xutao Li, Yifeng Zeng, Gao Cong, Yeow Meng Chee, and Quan Yuan. 2015. Personalized ranking metric embedding for next new POI recommendation. In Proceedings of the 24th International Joint Conference on Artificial Intelligence.
- Xutao Li, Gao Cong, Xiao-Li Li, Tuan-Anh Nguyen Pham, and Shonali Krishnaswamy. 2015. Rank-geofm: A ranking based geographical factorization method for point of interest recommendation. In Proceedings of the 38th International ACM SIGIR Conference on Research and Development in Information Retrieval. 433-442.
- Xin Liu, Yong Liu, and Xiaoli Li. 2016. Exploring the context of locations for personalized location recommendations.. In Proceedings of the IJCAI. 1188-1194.
- Tomás Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient estimation of word representations in vector space. In Proceedings of the International Conference on Learning Representations (ICLR'13).
- Matthew Peters, M. Neumann, M. Iyyer, M. Gardner, and L. Zettlemoyer. 2018. Deep contextualized word represen- tations. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers).
- Ruder Sebasian. 2017. An overview of multi-task learning in deep neural networks. arXiv:1706.05098. · arXiv
- J. J. Xu, K. Zheng, M. M. Chi, Y. Y. Zhu, and X. F. Zhou. 2015. Trajectory big data: Data, applications and techniques. Journal on Communications 36, 12 (2015), 97.
- Jia-Dong Zhang, Chi-Yin Chow, and Yanhua Li. 2014. Lore: Exploiting sequential influence for location recommen- dations. In Proceedings of the 22nd ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems. 103-112.
- Yu Zheng, Xing Xie, and Wei-Ying Ma. 2010. GeoLife: A collaborative social networking service among user, location and trajectory. IEEE Data Engineering Bulletin 33, 2 (2010), 32-39.
Cited by
Sorted by citations
A Multimodal Foundation Agent for Financial Trading: Tool-Augmented, Diversified, and Generalist
Zhang, W., Zhao, L., Xia, H., Sun, S., Sun, J., Qin, M., Li, X., Zhao, Y., Zhao, Y., Cai, X., et al. (2024) · Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining · doi:10.1145/3637528.3671801 · Cited by 74 · see citing works
- Authors
- Sun, H., Cao, C., Chu, X., Hu, T., Lu, J., He, L., Wang, Z., He, H., & Xiong, H.
- Affiliations
- Xi'an Jiaotong University (CN)
- Tencent (China) (CN)
- Hong Kong University of Science and Technology (HK)
- Venue
- ACM Transactions on Intelligent Systems and Technology
- Published
- 2023-11-14
- Volume
- 14
- Issue
- 6
- Pages
- 1-21
- ISSN
- 2157-6904 (ISSN-L)
- 2157-6912
- OpenAlex
- W4387188031
- Cited by
- 10
- References
- 30
- Citations by year
- 2025: 2 · 2024: 7 · 2023: 1
- FWCI
- 1.21
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- ACM Transactions on Intelligent Systems and Technology (publishedVersion)
- Funders
- National Key RD Program of China (2020YFB1406900, 2020AAA0107100)
- National Science Foundation of China (62072365)
- Science Foundation of Distinguished Young Scholars of Shaanxi (2022JC-48)
- Key Research and Development Program of Shaanxi (2022GY-332)
- Big Mobility Rhino-bird Special Research Program of Tencent (T160-CSIG-2022061300001)
- Innovation Capability Support Plan of Shaanxi (2021PT-010, 2022PT-33)
- Topics
- Physical Sciences › Computer Science › Signal Processing › Data Management and Algorithms
- Social Sciences › Transportation › Human Mobility and Location-Based Analysis
- Physical Sciences › Computer Science › Information Systems › Recommender Systems and Techniques
- Keywords
- check-in behavior, point-of-interest recommendation, social semantics, information dissemination, multi-task learning, spatio-temporal trajectories, social networks, pre-training
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