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Learning Graph-based Disentangled Representations for Next POI Recommendation

Published: 07 July 2022 Publication History

Abstract

Next Point-of-Interest (POI) recommendation plays a critical role in many location-based applications as it provides personalized suggestions on attractive destinations for users. Since users' next movement is highly related to the historical visits, sequential methods such as recurrent neural networks are widely used in this task for modeling check-in behaviors. However, existing methods mainly focus on modeling the sequential regularity of check-in sequences but pay little attention to the intrinsic characteristics of POIs, neglecting the entanglement of the diverse influence stemming from different aspects of POIs. In this paper, we propose a novel Disentangled Representation-enhanced Attention Network (DRAN) for next POI recommendation, which leverages the disentangled representations to explicitly model different aspects and corresponding influence for representing a POI more precisely. Specifically, we first design a propagation rule to learn graph-based disentangled representations by refining two types of POI relation graphs, making full use of the distance-based and transition-based influence for representation learning. Then, we extend the attention architecture to aggregate personalized spatio-temporal information for modeling dynamic user preferences on the next timestamp, while maintaining the different components of disentangled representations independent. Extensive experiments on two real-world datasets demonstrate the superior performance of our model to state-of-the-art approaches. Further studies confirm the effectiveness of DRAN in representation disentanglement.

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References

[1]
Alexander A. Alemi, Ian Fischer, Joshua V. Dillon, and Kevin Murphy. 2017. Deep Variational Information Bottleneck. In ICLR. 11396--11404.
[2]
Yoshua Bengio, Aaron Courville, and Pascal Vincent. 2013. Representation learning: A review and new perspectives. IEEE transactions on pattern analysis and machine intelligence (2013), 1798--1828.
[3]
Chen Cheng, Haiqin Yang, Michael R Lyu, and Irwin King. 2013. Where you like to go next: Successive point-of-interest recommendation. In IJCAI. 2605--2611.
[4]
Jie Feng, Yong Li, Chao Zhang, Funing Sun, Fanchao Meng, Ang Guo, and Depeng Jin. 2018. Deepmove: Predicting human mobility with attentional recurrent networks. In WWW. 1459--1468.
[5]
Shanshan Feng, Gao Cong, Bo An, and Yeow Meng Chee. 2017. Poi2vec: Geographical latent representation for predicting future visitors. In AAAI. 102--108.
[6]
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 IJCAI. 2069--2075.
[7]
Qing Guo, Zhu Sun, Jie Zhang, and Yin-Leng Theng. 2020. An attentional recurrent neural network for personalized next location recommendation. In AAAI. 83--90.
[8]
Jing He, Xin Li, Lejian Liao, Dandan Song, and William Cheung. 2016. Inferring a personalized next point-of-interest recommendation model with latent behavior patterns. In AAAI. 137--143.
[9]
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yongdong Zhang, and Meng Wang. 2020. Lightgcn: Simplifying and powering graph convolution network for recommendation. In SIGIR. 639--648.
[10]
Vineet John, Lili Mou, Hareesh Bahuleyan, and Olga Vechtomova. 2018. Disentangled representation learning for non-parallel text style transfer. arXiv preprint arXiv:1808.04339 (2018).
[11]
Wang-Cheng Kang and Julian McAuley. 2018. Self-attentive sequential recommendation. In ICDM. 197--206.
[12]
Thomas N Kipf and Max Welling. 2016. Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907 (2016).
[13]
Dejiang Kong and Fei Wu. 2018. HST-LSTM: A Hierarchical Spatial-Temporal Long-Short Term Memory Network for Location Prediction. In IJCAI. 2341-- 2347.
[14]
Huayu Li, Yong Ge, Defu Lian, and Hao Liu. 2017. Learning User's Intrinsic and Extrinsic Interests for Point-of-Interest Recommendation: A Unified Approach. In IJCAI. 2117--2123.
[15]
Jiacheng Li, Yujie Wang, and Julian McAuley. 2020. Time interval aware selfattention for sequential recommendation. In WSDM. 322--330.
[16]
Qimai Li, Zhichao Han, and Xiao-Ming Wu. 2018. Deeper insights into graph convolutional networks for semi-supervised learning. In AAAI. 3538--3545.
[17]
Ranzhen Li, Yanyan Shen, and Yanmin Zhu. 2018. Next point-of-interest recommendation with temporal and multi-level context attention. In ICDM. 1110-- 1115.
[18]
Yang Li, Tong Chen, Hongzhi Yin, and Zi Huang. 2021. Discovering collaborative signals for next POI recommendation with iterative Seq2Graph augmentation. arXiv preprint arXiv:2106.15814 (2021).
[19]
Defu Lian, Vincent W Zheng, and Xing Xie. 2013. Collaborative filtering meets next check-in location prediction. In WWW Companion Volume. 231--232.
[20]
Nicholas Lim, Bryan Hooi, See-Kiong Ng, Xueou Wang, Yong Liang Goh, Renrong Weng, and Jagannadan Varadarajan. 2020. STP-UDGAT: Spatial-TemporalPreference User Dimensional Graph Attention Network for Next POI Recommendation. In CIKM. 845--854.
[21]
Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan. 2016. Predicting the next location: A recurrent model with spatial and temporal contexts. In AAAI. 194-- 200.
[22]
Yiding Liu, Tuan-Anh Nguyen Pham, Gao Cong, andQuan Yuan. 2017. An experimental evaluation of point-of-interest recommendation in location-based social networks. Proceedings of the VLDB Endowment 10, 10 (2017), 1010--1021.
[23]
Yingtao Luo, Qiang Liu, and Zhaocheng Liu. 2021. STAN: Spatio-Temporal Attention Network for Next Location Recommendation. In WWW. 2177--2185.
[24]
Jianxin Ma, Chang Zhou, Peng Cui, Hongxia Yang, and Wenwu Zhu. 2019. Learning disentangled representations for recommendation. arXiv preprint arXiv:1910.14238 (2019).
[25]
Andriy Mnih and Russ R Salakhutdinov. 2008. Probabilistic matrix factorization. In NeurIPS. 1257--1264.
[26]
Steffen Rendle, Christoph Freudenthaler, and Lars Schmidt-Thieme. 2010. Factorizing personalized markov chains for next-basket recommendation. In WWW. 811--820.
[27]
Ke Sun, Tieyun Qian, Tong Chen, Yile Liang, Quoc Viet Hung Nguyen, and Hongzhi Yin. 2020. Where to go next: Modeling long-and short-term user preferences for point-of-interest recommendation. In AAAI. 214--221.
[28]
Gábor J Székely, Maria L Rizzo, and Nail K Bakirov. 2007. Measuring and testing dependence by correlation of distances. The annals of statistics 35, 6 (2007), 2769-- 2794.
[29]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, ukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In NeurIPS. 5998--6008.
[30]
Hao Wang, Huawei Shen, Wentao Ouyang, and Xueqi Cheng. 2018. Exploiting POI-Specific Geographical Influence for Point-of-Interest Recommendation. In IJCAI. 3877--3883.
[31]
Hongwei Wang, Miao Zhao, Xing Xie, Wenjie Li, and Minyi Guo. 2019. Knowledge Graph Convolutional Networks for Recommender Systems. In WWW. 3307--3313.
[32]
Xiang Wang, Xiangnan He, Meng Wang, Fuli Feng, and Tat-Seng Chua. 2019. Neural graph collaborative filtering. In SIGIR. 165--174.
[33]
Xiang Wang, Hongye Jin, An Zhang, Xiangnan He, Tong Xu, and Tat-Seng Chua. 2020. Disentangled graph collaborative filtering. In SIGIR. 1001--1010.
[34]
Shu Wu, Yuyuan Tang, Yanqiao Zhu, Liang Wang, Xing Xie, and Tieniu Tan. 2019. Session-based recommendation with graph neural networks. In AAAI. 346--353.
[35]
Min Xie, Hongzhi Yin, Hao Wang, Fanjiang Xu, Weitong Chen, and Sen Wang. 2016. Learning graph-based poi embedding for location-based recommendation. In CIKM. 15--24.
[36]
Mao Ye, Peifeng Yin, Wang-Chien Lee, and Dik-Lun Lee. 2011. Exploiting geographical influence for collaborative point-of-interest recommendation. In SIGIR. 325--334.
[37]
Hongzhi Yin, Bin Cui, Xiaofang Zhou, Weiqing Wang, Zi Huang, and Shazia Sadiq. 2016. Joint modeling of user check-in behaviors for real-time point-ofinterest recommendation. ACM Trans. Inf. Syst. 35, 2 (2016), 1--44.
[38]
Kangzhi Zhao, Yong Zhang, Hongzhi Yin, Jin Wang, Kai Zheng, Xiaofang Zhou, and Chunxiao Xing. 2020. Discovering Subsequence Patterns for Next POI Recommendation. In IJCAI. 3216--3222.
[39]
Pengpeng Zhao, Anjing Luo, Yanchi Liu, Fuzhen Zhuang, Jiajie Xu, Zhixu Li, Victor S Sheng, and Xiaofang Zhou. 2020. Where to go next: A spatio-temporal gated network for next poi recommendation. TKDE (2020).
[40]
Tianyu Zhu, Leilei Sun, and Guoqing Chen. 2021. Embedding Disentanglement in Graph Convolutional Networks for Recommendation. TKDE (2021).
[41]
Yu Zhu, Hao Li, Yikang Liao, Beidou Wang, Ziyu Guan, Haifeng Liu, and Deng Cai. 2017. What to Do Next: Modeling User Behaviors by Time-LSTM. In IJCAI. 3602--3608.

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  • (2025)MixRec: Heterogeneous Graph Collaborative FilteringProceedings of the Eighteenth ACM International Conference on Web Search and Data Mining10.1145/3701551.3703591(136-145)Online publication date: 10-Mar-2025
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    cover image ACM Conferences
    SIGIR '22: Proceedings of the 45th International ACM SIGIR Conference on Research and Development in Information Retrieval
    July 2022
    3569 pages
    ISBN:9781450387323
    DOI:10.1145/3477495
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    Published: 07 July 2022

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    Author Tags

    1. disentangled representation learning
    2. graph convolution networks
    3. next poi recommendation
    4. point-of-interest

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    • (2025)MixRec: Heterogeneous Graph Collaborative FilteringProceedings of the Eighteenth ACM International Conference on Web Search and Data Mining10.1145/3701551.3703591(136-145)Online publication date: 10-Mar-2025
    • (2025)GeM: Gaussian embeddings with Multi-hop graph transfer for next POI recommendationNeural Networks10.1016/j.neunet.2025.107290186(107290)Online publication date: Jun-2025
    • (2025)Simplified self-supervised learning for hybrid propagation graph-based recommendationNeural Networks10.1016/j.neunet.2025.107145185(107145)Online publication date: May-2025
    • (2025)Multi-Interest Granularity Guided Semi-Joint Learning for N-Successive POI RecommendationDatabase Systems for Advanced Applications10.1007/978-981-97-5779-4_9(131-146)Online publication date: 11-Jan-2025
    • (2024)An Expectation-Maximization framework for Personalized Itinerary Recommendation with POI Categories and Must-see POIsACM Transactions on Recommender Systems10.1145/36961143:1(1-33)Online publication date: 16-Sep-2024
    • (2024)MvStHgL: Multi-View Hypergraph Learning with Spatial-Temporal Periodic Interests for Next POI RecommendationACM Transactions on Information Systems10.1145/366465142:6(1-29)Online publication date: 19-Aug-2024
    • (2024)City Matters! A Dual-Target Cross-City Sequential POI Recommendation ModelACM Transactions on Information Systems10.1145/366428442:6(1-27)Online publication date: 19-Aug-2024
    • (2024)MCN4Rec: Multi-level Collaborative Neural Network for Next Location RecommendationACM Transactions on Information Systems10.1145/364366942:4(1-26)Online publication date: 22-Mar-2024
    • (2024)ROTAN: A Rotation-based Temporal Attention Network for Time-Specific Next POI RecommendationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671809(759-770)Online publication date: 25-Aug-2024
    • (2024)Diffusion-Based Cloud-Edge-Device Collaborative Learning for Next POI RecommendationsProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671743(2026-2036)Online publication date: 25-Aug-2024
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