Abstract
Next point-of-interest recommendation has become an increasingly significant requirement in location-based social networks. Recently, RNN-based methods have shown promising advantages in next POI recommendation due to their superior abilities in modeling sequential transitions of user behaviors. Despite their success, however, exploring complex correlations between POIs and capturing user dynamic preferences are still challenging issues. To overcome the limitations, we propose a novel framework named MPGI (Mining Preferences from Geographical and Interactive Correlations) for next POI recommendation. Specifically, we first design a POI correlation modeling layer to capture geographical distances and interactive correlations between all of POI pairs. Then, we fuse relevant signals from highly correlated POIs into target POI for high-quality POI representations. Furthermore, for user long- and short-term preferences modeling, we propose position-aware attention unites and attention network to dynamically select the most valuable information in check-in trajectories. Experimental results on two real-world datasets demonstrate that MPGI consistently outperforms the state-of-the-art methods.
Similar content being viewed by others
References
Halder S, Lim KH, Chan J, Zhang X (2022) Efficient itinerary recommendation via personalized POI selection and pruning. Knowl Inf Syst 64:963–993. https://doi.org/10.1007/s10115-021-01648-3
Zhang H, Gan M, Sun X (2021) Incorporating memory-based preferences and point-of-interest stickiness into recommendations in location-based social networks. ISPRS Int J Geo-Inf 10:36. https://doi.org/10.3390/ijgi10010036
Bahari Sojahrood Z, Taleai M (2021) A POI group recommendation method in location-based social networks based on user influence. Expert Syst Appl 171:114593. https://doi.org/10.1016/j.eswa.2021.114593
Christoforidis G, Kefalas P, Papadopoulos AN, Manolopoulos Y (2021) RELINE: point-of-interest recommendations using multiple network embeddings. Knowl Inf Syst 63:791–817. https://doi.org/10.1007/s10115-020-01541-5
Pang G, Wang X, Hao F et al (2020) Efficient point-of-interest recommendation with hierarchical attention mechanism. Appl Soft Comput J 96:106536. https://doi.org/10.1016/j.asoc.2020.106536
Zheng C, Tao D, Wang J, Cui L (2021) Memory augmented hierarchical attention network for next point-of-interest recommendation. IEEE Trans Comput Soc Syst 8:489–499. https://doi.org/10.1109/TCSS.2020.3036661
Chen M, Liu Y, Yu X (2014) NLPMM: a next location predictor with Markov modeling. Springer Int Publ 8444:186–197. https://doi.org/10.1007/978-3-319-06605-9_16
Cheng C, Yang H, Lyu MR, King I (2013) Where you like to go next: successive point-of-interest recommendation. In: Proceedings of the twenty-third international joint conference on artificial intelligence. pp 2605–2611
Liu T, Liao J, Wu Z et al (2020) Exploiting geographical-temporal awareness attention for next point-of-interest recommendation. Neurocomputing 400:227–237. https://doi.org/10.1016/j.neucom.2019.12.122
Zhou F, Dai Y, Gao Q et al (2021) Self-supervised human mobility learning for next location prediction and trajectory classification. Knowl Based Syst 228:107214. https://doi.org/10.1016/j.knosys.2021.107214
Wang H, Li P, Liu Y, Shao J (2021) Towards real-time demand-aware sequential POI recommendation. Inf Sci (Ny) 547:482–497. https://doi.org/10.1016/j.ins.2020.08.088
Huang L, Ma Y, Wang S, Liu Y (2021) An attention-based spatiotemporal LSTM network for Next POI recommendation. IEEE Trans Serv Comput 14:1585–1597. https://doi.org/10.1109/TSC.2019.2918310
Liu Q, Wu S, Wang L, Tan T (2016) Predicting the next location: a recurrent model with spatial and temporal contexts. Proc Thirtieth AAAI Conf Artif Intell 2016:194–200
Feng J, Li Y, Zhang C et al (2018) DeepMove: predicting human mobility with attentional recurrent networks. Proc World Wide Web Conf 2018:1459–1468
Zhang J, Liu X, Zhou X, Chu X (2021) Leveraging graph neural networks for point-of-interest recommendations. Neurocomputing 462:1–13. https://doi.org/10.1016/j.neucom.2021.07.063
Zhong T, Zhang S, Zhou F et al (2020) Hybrid graph convolutional networks with multi-head attention for location recommendation. World Wide Web 23:3125–3151. https://doi.org/10.1007/s11280-020-00824-9
Wang X, He X, Wang M, et al (2019) Neural graph collaborative filtering. In: Proceedings ofthe 42nd international ACMSIGIR conference on research and development in information retrieval. pp 165–174
Sarwar B, Karypis G, Konstan J, Riedl J (2001) Item-based collaborative filtering recommendation Algorithms. In: Proceedings of the 10th international conference on world wide web (WWW’01). pp 285–295
Li R, Shen Y, Zhu Y (2018) Next Point-of-interest recommendation with temporal and multi-level context attention. In: 2018 IEEE international conference on data mining. IEEE, pp 1110–1115
Zhu G, Wang Y, Cao J et al (2021) Neural attentive travel package recommendation via exploiting long-term and short-term behaviors. Knowl Based Syst 211:106511. https://doi.org/10.1016/j.knosys.2020.106511
Sun K, Qian T, Chen T, et al (2020) Where to go next: modeling long- and short-term user preferences for point-of-interest recommendation. In: Proceedings of the thirty-fourth AAAI conference on artificial intelligence. pp 214–221
Ren X, Song M, Haihong E, Song J (2017) Context-aware probabilistic matrix factorization modeling for point-of-interest recommendation. Neurocomputing 241:38–55. https://doi.org/10.1016/j.neucom.2017.02.005
Yuan Q, Cong G, Ma Z, et al (2013) 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
Yao L, Sheng QZ, Qin Y, et al (2015) Context-aware point-of-interest recommendation using Tensor Factorization with social regularization. In: Proceedings of the 38th international ACM SIGIR conference on research and development in information retrieval. pp 1007–1010
Li X, Cong G, Li XL, et al (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. pp 433–442
Gan M, Gao L (2019) Discovering memory-based preferences for POI recommendation in location-based social networks. ISPRS Int J Geo-Inf 8:279. https://doi.org/10.3390/ijgi8060279
Davtalab M, Alesheikh AA (2021) A POI recommendation approach integrating social spatio-temporal information into probabilistic matrix factorization. Knowl Inf Syst 63:65–85. https://doi.org/10.1007/s10115-020-01509-5
Ma Y, Gan M (2020) Exploring multiple spatio-temporal information for point-of-interest recommendation. Soft Comput 24:18733–18747. https://doi.org/10.1007/s00500-020-05107-z
Liu Q, Mu L, Sugumaran V et al (2021) Pair-wise ranking based preference learning for points-of-interest recommendation. Knowl Based Syst 225:107069. https://doi.org/10.1016/j.knosys.2021.107069
Yin H, Wang W, Wang H et al (2017) Spatial-aware hierarchical collaborative deep learning for POI recommendation. IEEE Trans Knowl Data Eng 29:2537–2551. https://doi.org/10.1109/TKDE.2017.2741484
Liu Y, Yang Z, Li T, Wu D (2021) A novel POI recommendation model based on joint spatiotemporal effects and four-way interaction. Appl Intell 52:5310–5324. https://doi.org/10.1007/s10489-021-02677-9
Chen L, Cao J, Chen H et al (2021) Attentive multi-task learning for group itinerary recommendation. Knowl Inf Syst 63:1687–1716. https://doi.org/10.1007/s10115-021-01567-3
Feng S, Li X, Zeng Y, et al (2015) Personalized ranking metric embedding for next new POI recommendation. In: Proceedings of the twenty-fourth international joint conference on artificial intelligence. pp 2069–2075
Yu F, Cui L, Guo W et al (2020) A category-aware deep model for successive POI recommendation on sparse check-in data. Proc World Wide Web Conf 2020:1264–1274
Wu Y, Li K, Zhao G, Qian x (2022) Personalized long- and short-term preference learning for next POI recommendation. IEEE Trans Knowl Data Eng 34:1944–1957. https://doi.org/10.1109/TKDE.2020.3002531
Liu X, Yang Y, Xu Y et al (2022) Real-time POI recommendation via modeling long- and short-term user preferences. Neurocomputing 467:454–464. https://doi.org/10.1016/j.neucom.2021.09.056
Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser IP (2017) Attention is all you need. In: Proceedings of the advances in neural information processing systems. pp 5998–6008
Zhou G, Mou N, Fan Y, et al (2019) Deep interest evolution network for click-through rate prediction. In: Proceedings of the thirty-third AAAI conference on artificial intelligence. pp 5941–5948
Wu S, Tang Y, Zhu Y, et al (2019) Session-based recommendation with graph neural networks. In: Proceedings of the thirty-third AAAI conference on artificial intelligence. pp 346–353
Liu L, Wang L, Lian T (2021) CaSe4SR: using category sequence graph to augment session-based recommendation. Knowl Based Syst 212:106558. https://doi.org/10.1016/j.knosys.2020.106558
Shi M, Shen D, Kou Y et al (2021) Attentional memory network with correlation-based embedding for time-aware POI recommendation. Knowl Based Syst 214:106747. https://doi.org/10.1016/j.knosys.2021.106747
Zheng J, Li Q, Liao J (2021) Heterogeneous type-specific entity representation learning for recommendations in e-commerce network. Inf Process Manag 58:102629. https://doi.org/10.1016/j.ipm.2021.102629
Ni J, Huang Z, Yu C et al (2021) Comparative convolutional dynamic multi-attention recommendation model. IEEE Trans Neural Netw Learn Syst. https://doi.org/10.1109/TNNLS.2021.3053245
Ma Y, Mao J, Ba Z, Li G (2020) Location recommendation by combining geographical, categorical, and social preferences with location popularity. Inf Process Manag 57:102251. https://doi.org/10.1016/j.ipm.2020.102251
Feng Y, Lv F, Shen W et al (2019) Deep session interest network for click-through rate prediction. IJCAI Int J Conf Artif Intell. https://doi.org/10.24963/ijcai.2019/319
Thaipisutikul T, Shih TK (2021) A novel context-aware recommender system based on a deep sequential learning approach (CReS). Neural Comput Appl 33:11067–11090. https://doi.org/10.1007/s00521-020-05640-w
Xu H, Huang C, Xu Y, et al (2020) Global context enhanced social recommendation with hierarchical graph neural networks. In: Proceedings ofthe 43rd international ACM SIGIR conference on research and development in information retrieval pp 701–710
Zhong J, Ma C, Zhou J, Wang W (2020) Pdpnn: modeling user personal dynamic preference for next point-of-interest recommendation. Int Conf Comput Sci 2020:45–57
Yang D, Zhang D, Zheng VW, Yu Z (2015) Modeling user activity preference by leveraging user spatial temporal characteristics in LBSNs. IEEE Trans Syst Man Cybern Syst 45:129–142. https://doi.org/10.1109/TSMC.2014.2327053
Schuster M, Paliwal KK (1997) Bidirectional recurrent neural networks. IEEE Trans Signal Process 45:2673–2681. https://doi.org/10.1109/78.650093
Luo Y, Liu Q, Liu Z (2021) STAN: spatio-temporal attention network for next location recommendation. Proc of the World Wide Web Conf 2021:2177–2185
Van Der Maaten L, Hinton G (2008) Visualizing data using t-SNE. J Mach Learn Res 9:2579–2605
Acknowledgements
This work was supported by the National Natural Science Foundation of China under Grants Nos. 72271024, 71871019, 71471016.
Author information
Authors and Affiliations
Contributions
J.R. contributed to data curation, methodology, validation, writing—original draft. M.G. contributed to conceptualization, resources, investigation, writing—review and editing, supervision, and funding acquisition.
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
Springer Nature or its licensor holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
About this article
Cite this article
Ren, J., Gan, M. Mining dynamic preferences from geographical and interactive correlations for next POI recommendation. Knowl Inf Syst 65, 183–206 (2023). https://doi.org/10.1007/s10115-022-01749-7
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10115-022-01749-7