结合通用轨迹图和多偏好的兴趣点推荐方法

doi:10.3969/j.issn.0255-8297.2025.05.005

Abstract

Abstract: Traditional point of interest (POI) recommendation methods often fail to fully mine the relationships between users and POIs, resulting in limited capacity to capture user preferences. Although graph enhancement method offers improved relational modeling, it is susceptible to noise, which reduces the recommendation precision. To address these challenges, this paper proposes a POI recommendation method named combining universal trajectory maps and multiple preferences. Firstly, a weighted bipartite graph between users and POIs is constructed. Graph convolutional network (GCN) is used to extract the interactive relationship between users and POIs to learn users’ interest preferences. Users are clustered using the obtained interest preferences. The general trajectory maps of the same type of users are built to reduce the impact of noise information. GCN is further used to mine the group features of different types of users and enrich feature representation. Secondly, the group features are combined with time-aware category information and spatio-temporal context from the user’s current trajectory. Transformer model is used to capture deep behavioral preferences. Finally, a non-linear additive function is used to dynamically combine interest preferences with current behavior preferences to fully capture user preferences and generate POI recommendations. The validity of the proposed method is verified on real data sets.

Key words: point of interest (POI), user preferences, recommendation system, graph convolutional network (GCN), universal trajectory map

CLC Number:

P751.1

LU Jing, GE Cong. Point of Interest Recommendation Method Combining Universal Trajectory Maps and Multiple Preferences[J]. Journal of Applied Sciences, 2025, 43(5): 771-784.

References

[1] 任星怡, 宋美娜, 宋俊德. 基于位置社交网络的上下文感知的兴趣点推荐[J]. 计算机学报, 2017, 40(4): 824-841. Ren X Y, Song M N, Song J D. Context-aware point-of-interest recommendation in locationbased social networks [J]. Chinese Journal of Computers, 2017, 40(4): 824-841. (in Chinese)
[2] Lian D F, Ge Y, Zhang F Z, et al. Content-aware collaborative filtering for location recommendation based on human mobility data [C]//2015 IEEE International Conference on Data Mining, 2015: 261-270.
[3] Rendle S, Freudenthaler C, Schmidt-Thieme L. Factorizing personalized Markov chains for next-basket recommendation [C]//19th International Conference on World Wide Web, 2010: 811-820.
[4] Xie M, Yin H Z, Wang H, et al. Learning graph-based POI embedding for location-based recommendation [C]//25th ACM International on Conference on Information and Knowledge Management, 2016: 15-24.
[5] Wang H W, Zhao M, Xie X, et al. Knowledge graph convolutional networks for recommender systems [C]//The World Wide Web Conference, 2019: 3307-3313.
[6] Halilaj L, Lüttin J, Rothermel S, et al. Towards a knowledge graph-based approach for context-aware points-of-interest recommendations [C]//36th Annual ACM Symposium on Applied Computing, 2021: 1846-1854.
[7] Liu Q, Wu S, Wang L, et al. Predicting the next location: a recurrent model with spatial and temporal contexts [C]//Thirtieth AAAI Conference on Artificial Intelligence, 2016:194-200.
[8] Wu Y X, Li K, Zhao G S, et al. Long- and short-term preference learning for next POI recommendation [C]/ 28th ACM International Conference on Information and Knowledge Management, 2019: 2301-2304.
[9] Liu Y, Zhu H P, Chen Y, et al. Long- and short-term preference model based on graph embedding for sequential recommendation [C]//DASFAA 2020 International Workshops, 2020: 241-257.
[10] Zhao P P, Luo A J, Liu Y C, et al. Where to go next: a spatio-temporal gated network for next POI recommendation [C]//IEEE Transactions on Knowledge and Data Engineering, 2022: 2512-2524.
[11] Cui Q, Tang Y Y, Wu S, et al. Distance2Pre: personalized spatial preference for next pointof-interest prediction [C]//Advances in Knowledge Discovery and Data Mining, 2019: 289-301.
[12] Lian D F, Wu Y J, Ge Y, et al. Geography-aware sequential location recommendation [C]//26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, 2020: 2009-2019.
[13] Luo Y T, Liu Q, Liu Z C. STAN: spatio-temporal attention network for next location recommendation [C]//The World Wide Web Conference, 2021: 2177-2185.
[14] Yang S, Liu J M, Zhao K Q. GETNext: trajectory flow map enhanced transformer for next POI recommendation [C]//45th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2022: 1144-1153.
[15] Wu Y X, Li K, Zhao G S, et al. Long- and short-term preference learning for next POI recommendation [C]//28th ACM International Conference on Information and Knowledge Management, 2019: 2301-2304.
[16] Sun K, Qian T Y, Chen T, et al. Where to go next: modeling long- and short-term user preferences for point-of-interest recommendation [J]. AAAI Conference on Artificial Intelligence, 2020, 34(1): 214-221.
[17] Wu Y X, Li K, Zhao G S, et al. Personalized long- and short-term preference learning for next POI recommendation [J]. IEEE Transactions on Knowledge and Data Engineering, 2022, 34(4): 1944-1957.
[18] Wang X, He X N, Wang M, et al. Neural graph collaborative filtering [C]//42nd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2019: 165-174.
[19] He X N, Deng K, Wang X, et al. LightGCN: simplifying and powering graph convolution network for recommendation [C]//43rd International ACM SIGIR Conference on Research and Development in Information Retrieval, 2020: 639-648.
[20] Zahra S, Ali Ghazanfar M, Khalid A, et al. Novel centroid selection approaches for KMeans-clustering based recommender systems [J]. Information Sciences, 2015, 320: 156-189.
[21] Peñaloza V. Time2Vec embedding on a Seq2Seq Bi-directional LSTM network for pedestrian trajectory prediction [J]. Research on Computing Science, 2020, 149(11): 249-260.
[22] Tobler W R. A computer movie simulating urban growth in the Detroit Region [J]. Economic Geography, 1970, 46: 234.
[23] Xie J Y, Chen Z Z. Hierarchical transformer with spatio-temporal context aggregation for next point-of-interest recommendation [J]. ACM Transactions on Information Systems, 2024, 42(2): 1-30.
[24] Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need [C]//Conference and Workshop on Neural Information Processing Systems, 2017: 5998-6008.

Point of Interest Recommendation Method Combining Universal Trajectory Maps and Multiple Preferences

PDF

Knowledge

Abstract

Cite this article

share this article

References

Related Articles 3

Recommended Articles

Metrics

Comments

[1]	SU Zhan, YANG Haochuan, AI Jun. Recommendation Algorithm Based on Fuzzy Preference Label Vector [J]. Journal of Applied Sciences, 2024, 42(3): 525-539.
[2]	SU Zhan, CHEN Xueqian, AI Jun, HUANG Zhong. Recommendation Algorithm Based on User Similarity Selection and Label Distance [J]. Journal of Applied Sciences, 2023, 41(6): 940-957.
[3]	JI Deqiang, WANG Hairong, CHE Miao, WANG Jiaxin. KNN-GWD Recommendation Model and Its Application [J]. Journal of Applied Sciences, 2022, 40(1): 145-154.