MvStHgL: Multi-View Hypergraph Learning with Spatial-Temporal Periodic Interests for Next POI Recommendation

Jingmin An; Ming Gao; Jiafu Tang

doi:10.1145/3664651

journal article Aug 19, 2024

MvStHgL: Multi-View Hypergraph Learning with Spatial-Temporal Periodic Interests for Next POI Recommendation

Jingmin An

Ming Gao

Jiafu Tang

ACM Transactions on Information Systems Vol. 42 No. 6 pp. 1-29 · Association for Computing Machinery (ACM)

View at Publisher Save 10.1145/3664651

Abstract

Providing potential next point-of-interest (POI) suggestions for users has become a prominent task in location-based social networks, which receives more and more attention from the industry and academia and it remains challenging due to highly dynamic and personalized interactions in user movements. Currently, state-of-the-art works develop various graph- and sequential-based learning methods to model user-POI interactions and transition regularities. However, there are still two significant shortcomings in these works: (1) ignoring personalized spatial and temporal-aspect interactive characteristics capable of exhibiting periodic interests of users and (2) insufficiently leveraging the sequential patterns of interactions for beyond-pairwise high-order collaborative signals among users’ sequences. To jointly address these challenges, we propose a novel multi-view hypergraph learning with spatial-temporal periodic interests for next POI recommendation (MvStHgL). In the local view, we attempt to learn the POI representation of each interaction via jointing periodic characteristics of spatial and temporal aspects. In the global view, we design a hypergraph by regarding interactive sequences as hyperedges to capture high-order collaborative signals across users, for further POI representations. More specifically, the output of POI representations in the local view is used for the initialized embedding, and the aggregation and propagation in the hypergraph are performed by a novel Node-to-Hypergraph-to-Node scheme. Furthermore, the captured POI embeddings are applied to achieve sequential dependency modeling for next POI prediction. Extensive experiments on three real-world datasets demonstrate that our proposed model outperforms the state-of-the-art models.

Topics

No keywords indexed for this article. Browse by subject →

References

61

[1]

10.1145/3423322

[2]

10.1109/tcyb.2020.3000733

[3]

10.1109/tse.2023.3252259

[4]

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 (IJCAI’13). 2605–2611. DOI: https://doi.org/ijcai.org/Abstract/13/384

[5]

10.1145/3459637.3482189

[6]

10.1109/tpami.2009.75

[7]

10.1145/3488560.3498400

[8]

10.1145/3485447.3512077

[9]

10.1145/3178876.3186058

[10]

POI2Vec: Geographical Latent Representation for Predicting Future Visitors

Shanshan Feng, Gao Cong, Bo AN et al.

Proceedings of the AAAI Conference on Artificial I... 10.1609/aaai.v31i1.10500

[11]

Hypergraph Neural Networks

Yifan Feng, Haoxuan You, Zizhao Zhang et al.

Proceedings of the AAAI Conference on Artificial I... 10.1609/aaai.v33i01.33013558

[12]

HGNN+: General Hypergraph Neural Networks

Yue Gao, Yifan Feng, Shuyi Ji et al.

IEEE Transactions on Pattern Analysis and Machine... 10.1109/tpami.2022.3182052

[13]

10.1145/3568395

[14]

10.1609/aaai

[15]

10.1145/3543507.3583402

[16]

Deep Residual Learning for Image Recognition

Kaiming He, Xiangyu Zhang, Shaoqing Ren et al.

2016 IEEE Conference on Computer Vision and Patter... 10.1109/cvpr.2016.90

[17]

10.1145/3397271.3401063

[18]

Long Short-Term Memory

Sepp Hochreiter, Jürgen Schmidhuber

Neural Computation 10.1162/neco.1997.9.8.1735

[19]

10.1145/3477495.3531801

[20]

10.1109/tkde.2020.3033841

[21]

10.1145/3336191.3371786

[22]

10.1145/3394486.3403179

[23]

10.24963/ijcai.2021/206

[24]

10.1145/3477495.3531794

[25]

10.1109/tkde.2018.2872063

[26]

10.1145/3394486.3403252

[27]

10.1145/3477495.3531989

[28]

10.1145/3485447.3512104

[29]

10.1109/tkde.2020.3010022

[30]

Meng Liu Ke Liang Bin Xiao Sihang Zhou Wenxuan Tu Yue Liu Xihong Yang and Xinwang Liu. Self-Supervised Temporal Graph learning with Temporal and Structural Intensity Alignment. arXiv:2302.07491v2. DOI: 10.48550/arXiv.2302.07491 10.48550/arxiv.2302.07491

[31]

10.1109/tpami.2018.2879108

[32]

10.1145/3442381.3449998

[33]

10.1016/j.ins.2021.08.086

[34]

10.1109/tsmc.2022.321843

[35]

10.1145/3295499

[36]

10.1109/tkde.2022.3221873

[37]

10.1145/3534678.3539383

[38]

Where to Go Next: Modeling Long- and Short-Term User Preferences for Point-of-Interest Recommendation

Ke Sun, Tieyun Qian, Yile Liang et al.

Proceedings of the AAAI Conference on Artificial I... 10.1609/aaai.v34i01.5353

[39]

Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017. Attention is All You Need. In Proceedings of the Advances in Neural Information Processing Systems (NeurIPS’17), Vol. 30. 5998–6008. Retrieved from https://proceedings.neurips.cc/paper/2017/ile/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf

[40]

10.1145/3592853

[41]

10.1109/icde53745.2022.00202

[42]

10.1109/tkde.2023.3243239

[43]

10.1109/tnnls.2017.2677968

[44]

10.1287/ijoc.2023.1286

[45]

10.1145/3366423.3380186

[46]

10.1145/3477495.3532012

[47]

10.1145/3539618.3591634

[48]

10.1145/3513092

[49]

10.1145/3544105

[50]

10.1109/tpami.2022.3178156

Showing 50 of 61 references

Metrics

18

Citations

61

References

Details

Published: Aug 19, 2024
Vol/Issue: 42(6)
Pages: 1-29
License: View

Authors

J

Jingmin An

School of Management Science and Engineering, Key Laboratory of Big Data Management Optimization and Decision of Liaoning Province, Dongbei University of Finance and Economics, Dalian, China

M

Ming Gao

School of Management Science and Engineering, Key Laboratory of Big Data Management Optimization and Decision of Liaoning Province, Dongbei University of Finance and Economics, Dalian, China

J

Jiafu Tang

School of Management Science and Engineering, Key Laboratory of Big Data Management Optimization and Decision of Liaoning Province, Dongbei University of Finance and Economics, Dalian, China

Funding

National Natural Science Foundation of China Award: 72293563 and 71831003

Dalian Scientific and Technological Talents Innovation Support Plan Award: 2022RG17

Basic Scientific Research Project of Liaoning Provincial Department of Education Award: JYTZD2023050 and JYTMS20231870

Cite This Article

Jingmin An, Ming Gao, Jiafu Tang (2024). MvStHgL: Multi-View Hypergraph Learning with Spatial-Temporal Periodic Interests for Next POI Recommendation. ACM Transactions on Information Systems, 42(6), 1-29. https://doi.org/10.1145/3664651

MvStHgL: Multi-View Hypergraph Learning with Spatial-Temporal Periodic Interests for Next POI Recommendation

You May Also Like