研究论文

基于图神经网络的兴趣点推荐方法研究进展

  • 方金凤 ,
  • 陈祖颐
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  • 辽宁工程技术大学电子与信息工程学院, 葫芦岛 125105

方金凤,讲师,研究方向为时空大数据、兴趣点推荐、轨迹预测等,电子信箱:

收稿日期: 2024-01-05

  网络出版日期: 2025-06-13

基金资助

辽宁省教育厅理工类项目(JYTQN2023211)

版权

版权所有,未经授权,不得转载。

Research progress on point-of-interest recommendation methods based on graph neural networks

  • Jinfeng FANG ,
  • Zuyi CHEN
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  • School of Electronic and Information Engineering, Liaoning Technical University, Huludao 125105, China

Received date: 2024-01-05

  Online published: 2025-06-13

Copyright

All rights reserved. Unauthorized reproduction is prohibited.

摘要

交叉学科是新科学的生长点,是科学发展的必然趋势。基于位置服务的重要应用——兴趣点推荐,作为计算机学科与地理信息学科相交叉的研究课题,对于推动2个学科在时空数据分析等相关领域的交叉融合研究具有重要作用。分析了兴趣点推荐的影响因素即地理位置、时间因素、社交关系和流行度,重点阐述了基于图神经网络的兴趣点推荐方法,包括基于图注意力网络、图卷积网络、图自编码器的推荐,并对其特点进行对比;讨论了在兴趣点推荐中存在的一些关键挑战,如数据稀疏性、冷启动问题和用户动态偏好问题,并针对各项挑战提出相应的解决思路,提出了结合多种影响因素的推荐,跨领域推荐以及动态偏好推荐的发展趋势。

本文引用格式

方金凤 , 陈祖颐 . 基于图神经网络的兴趣点推荐方法研究进展[J]. 科技导报, 2025 , 43(9) : 76 -83 . DOI: 10.3981/j.issn.1000-7857.2024.01.00099

1
Zhang Q R , Yang P , Yu J L , et al. A survey on point-of- interest recommendation: Models, architectures, and security[J]. IEEE Transactions on Knowledge and Data Engineering, 2025, 1- 20.

2
Gan M X , Ma Y X . Mapping user interest into hyper-spheri-cal space: A novel POI recommendation method[J]. Informa-tion Processing & Management, 2023, 60(2): 103169.

3
Xu C H , Ding A S , Zhao K D . A novel POI recommendation method based on trust relationship and spatial-temporal factors[J]. Electronic Commerce Research and Applications, 2021, 48: 101060.

DOI

4
Wang E , Xu Y B , Yang Y J , et al. Zone-enhanced spatio-temporal representation learning for urban POI recom-mendation[J]. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(9): 9628- 9641.

DOI

5
张稳, 伊华伟, 兰洁, 等. 融合时空-社交-顺序影响的多维兴趣点推荐[J]. 计算机工程与设计, 2024, 45(9): 2704- 2711.

6
Zhu J , Lin H F , Gou Z N , et al. A dynamic and timely point-of-interest recommendation based on spatio-temporal influences, timeliness feature and social relationships[J]. ISPRS International Journal of Geo-Information, 2025, 14(2): 68.

DOI

7
Dietz L W , Sánchez P , Bellogín A . Understanding the influ-ence of data characteristics on the performance of point-of-interest recommendation algorithms[J]. Information Technology & Tourism, 2025, 27(1): 75- 124.

DOI

8
Wang Z H , Höpken W , Jannach D . A survey on point-of-interest recommendations leveraging heterogeneous data[J]. Information Technology & Tourism, 2025, 27(1): 29- 73.

DOI

9
Song X Y , Liu Z Z , Meng L Q , et al. Accurate POI recom-mendation for random groups with improved graph neural networks and a multi-negotiation model[J]. Scientific Reports, 2025, 15(1): 7531.

DOI

10
Li Z Y, Cheng W, Xiao H Q, et al. You are what and where you are: Graph enhanced attention network for explainable POI recommendation[C]//Proceedings of the 30th ACM International Conference on Information & Knowledge Management. New York: ACM, 2021: 3945-3954.

11
Zhang J Y , Liu X , Zhou X F , et al. Leveraging graph neural networks for point-of-interest recommendations[J]. Neuro-computing, 2021, 462: 1- 13.

12
Shi M H , Shen D R , Kou Y , et al. Attentional memory network with correlation-based embedding for time-aware POI recommendation[J]. Knowledge-Based Systems, 2021, 214: 106747.

DOI

13
Zhang Z H , Zhu J H , Yue C B . Session-based graph atten-tion POI recommendation network[J]. Wireless Communica-tions and Mobile Computing, 2022, 2022(1): 6557936.

14
刘志中, 李林霞, 孟令强. 基于混合图神经网络的个性化POI推荐方法研究[J]. 南京大学学报(自然科学版), 2023, 59(3): 373- 387.

15
Wang X L , Wang D J , Yu D J , et al. Intent-aware graph neural network for point-of-interest embedding and recom-mendation[J]. Neurocomputing, 2023, 557: 126734.

DOI

16
Wu Y S , Jin X , Huang H P . Muti-channel graph attention networks for POI recommendation[J]. Journal of Intelligent & Fuzzy Systems, 2023, 44(5): 8375- 8385.

17
Gong W H , Zheng K C , Zhang S B , et al. Deep pairwise learning for user preferences via dual graph attention model in location-based social networks[J]. Expert Systems with Applications, 2023, 227: 120222.

DOI

18
Fan X H , Hua Y X , Cao Y B , et al. Capturing dynamic inter-ests of similar users for POI recommendation using self-attention mechanism[J]. Sustainability, 2023, 15(6): 5034.

DOI

19
Fu J R , Gao R , Yu Y H , et al. Contrastive graph learning long and short-term interests for POI recommendation[J]. Expert Systems with Applications, 2024, 238: 121931.

DOI

20
Zhang J K , Ma W M . Hybrid structural graph attention network for POI recommendation[J]. Expert Systems with Applications, 2024, 248: 123436.

DOI

21
Meng L Q , Liu Z Z , Chu D H , et al. POI recommendation for occasional groups Based on hybrid graph neural networks[J]. Expert Systems with Applications, 2024, 237: 121583.

DOI

22
Li Y . A POI recommendation algorithm based on the hetero-geneous graph convolution network[J]. Scientific Program-ming, 2022, 2022(1): 9154712.

23
Wu H. A POI recommendation model with temporal-regional based graph representation learning[C]//Proceed-ings of IEEE 5th International Conference on Information Systems and Computer Aided Education (ICISCAE). Dalian: IEEE, 2022: 790-794.

24
Zhang S Z , Bai Z J , Li P , et al. Multi-graph convolutional network for fine-grained and personalized POI recommenda-tion[J]. Electronics, 2022, 11(18): 2966.

DOI

25
Wu Z Y , Xu N . Point-of-interest recommendation model based on graph convolutional neural network[J]. Wireless Communications and Mobile Computing, 2022(1): 7638117.

26
Mo F , Yamana H . EPT-GCN: Edge propagation-based time-aware graph convolution network for POI recommenda-tion[J]. Neurocomputing, 2023, 543: 126272.

DOI

27
Liu J T , Yi H W , Gao Y X , et al. Personalized point- of-interest recommendation using improved graph convolu-tional network in location-based social network[J]. Electron-ics, 2023, 12(16): 3495.

28
Gan Y , Hu Z Y . Fusion privacy protection of graph neural network points of interest recommendation[J]. International Journal of Advanced Computer Science and Applications, 2023, 14(4): 548- 556.

29
Anjiri S N , Ding D R , Song Y . HyGate-GCN: Hybrid-Gate-Based Graph Convolutional Networks with dynamical ratings estimation for personalized POI recommendation[J]. Expert Systems with Applications, 2024, 258: 125217.

DOI

30
Luan W J, Wang X Y, Qi L, et al. A GCN-based trip recom-mendation method incorporating reverse effect[C]//Proceed-ings of IEEE International Conference on Systems, Man, and Cybernetics (SMC). Kuching: IEEE, 2024: 1660-1665.

31
Pan L , Wei J Y , Lu Y J , et al. Travel interest point recom-mendation algorithm based on collaborative filtering and graph convolutional neural networks[J]. Computing and Informatics, 2024, 43(6): 1516- 1538.

DOI

32
闵昭浩, 张. 融合地理和时空信息的对比兴趣点推荐方法[J]. 计算机工程与设计, 2025, 46(2): 368- 375.

33
Zhao Z Y, Wang C J, Xu K L, et al. HyperMST: Multi-scale spatio-temporal hypercorrelation network for POI recom-mendation[C]//Proceedings of ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Hyderabad: IEEE, 2025: 1-5.

34
Lian X Q, Mi J C, Gao C, et al. Research on point-of-inter-est recommendation method based on graph autoencoders and long short-term preferences[C]//Proceedings of the 4th International Conference on Artificial Intelligence and Computer Engineering. New York: ACM, 2023: 864-869.

35
Xu Q D, Shen F M, Liu L, et al. GraphCAR: Content-aware multimedia recommendation with graph autoencoder[C]//Proceedings of the 41st International ACM SIGIR Confer-ence. Ann Arbor, USA: Association for Computing Machin-ery, 2018: 981-984.

36
Chang B R, Jang G, Kim S, et al. Learning graph-based geographical latent representation for point-of-interest recommendation[C]//Proceedings of the 29th ACM Interna-tional Conference on Information & Knowledge Manage-ment. New York: ACM, 2020: 135-144.

37
Yu F Q, Cui L Z, Guo W, et al. A category-aware deep model for successive POI recommendation on sparse check-in data[C]//Proceedings of The Web Conference 2020. New York: ACM, 2020: 1264-1274.

38
刘金鑫. 自编码器在推荐系统中的应用研究[D]. 天津: 天津理工大学, 2022.

39
Gan M X , Zhang H . VIGA: A variational graph autoencoder model to infer user interest representations for recommenda-tion[J]. Information Sciences, 2023, 640: 119039.

DOI

40
Abinaya S , Alphonse A S , Abirami S , et al. Enhancing context-aware recommendation using trust-based contex-tual attentive autoencoder[J]. Neural Processing Letters, 2023, 55(5): 6843- 6864.

DOI

41
Wang W , Suo X Y , Wei X Y , et al. HGATE: Heteroge-neous graph attention auto-encoders[J]. IEEE Transactions on Knowledge and Data Engineering, 2021, 35(4): 3938- 3951.

42
Zhu G X , Cao J , Chen L , et al. A multi-task graph neural network with variational graph auto-encoders for session-based travel packages recommendation[J]. ACM Transac-tions on the Web, 2023, 17(3): 1- 30.

43
温雯, 邓峰颖, 郝志峰, 等. 时空邻域感知的时序兴趣点推荐[J]. 计算机科学与探索, 2024, 18(7): 1865- 1878.

44
Zhang H Y , Shi Z X , Li M , et al. MaskPOI: A POI represen-tation learning method using graph mask modeling[J]. Elec-tronics, 2025, 14(7): 1242.

45
Xun Y L , Wang Y J , Zhang J F , et al. Higher-order embed-ded learning for heterogeneous information networks and adaptive POI recommendation[J]. Information Processing & Management, 2024, 61(4): 103763.

46
Zhang X, Ye Z M, Lu J F, et al. Fine-grained prefe-rence-aware personalized federated POI recommendation with data sparsity[C]//Proceedings of the 46th International ACM SIGIR Conference on Research and Development in Information Retrieval. New York: ACM, 2023: 413-422.

47
Liu C Y, Zhang H L, Tian Z S, et al. A generative- augmented deep matrix factorization model for POI recom-mendations[C]//Proceedings of ICASSP 2025-2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). Hyderabad: IEEE, 2025: 1-5.

48
Noorian A . A personalized context and sequence aware point of interest recommendation[J]. Multimedia Tools and Appli-cations, 2024, 83(32): 77565- 77594.

DOI

49
Anijri S N , Ding D R , Song Y , et al. A multiplex hyper-graph attribute-based graph collaborative filtering for cold-start POI recommendation[J]. IEEE Transactions on Big Data, 2025, 1- 16.

50
Bayram F , Ahmed B S , Kassler A . From concept drift to model degradation: An overview on performance-aware drift detectors[J]. Knowledge-Based Systems, 2022, 245: 108632.

DOI

51
Fan X Y , Ji Y Q , Hui B . A dynamic preference recommen-dation model based on spatiotemporal knowledge graphs[J]. Complex & Intelligent Systems, 2024, 11(1): 46.

52
Ni X L , Xiong F , Pan S R , et al. Community preserving social recommendation with cyclic transfer learning[J]. ACM Transactions on Information Systems, 2024, 42(3): 1- 36.

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