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Graph-Flashback Network for Next Location Recommendation

Published: 14 August 2022 Publication History

Abstract

Next Point-of Interest (POI) recommendation plays an important role in location-based applications, which aims to recommend the next POIs to users that they are most likely to visit based on their historical trajectories. Existing methods usually use rich side information, or customized POI graphs to capture the sequential patterns among POIs. However, the graphs only focus on connectivity between POIs. Few studies propose to explicitly learn a weighted POI graph, which could reflect the transition patterns among POIs and show the importance of its different neighbors for each POI. In addition, these approaches simply utilize the user characteristics for personalized POI recommendation without sufficient consideration. To this end, we construct a novel User-POI Knowledge Graph with strong representation ability, called Spatial-Temporal Knowledge Graph (STKG). STKG is used to learn the representations of each node (i.e., user, POI) and each edge. Then, we design a similarity function to construct our POI transition graph based on the learned representations. To incorporate the learned graph into sequential model, we propose a novel network Graph-Flashback for recommendation. Graph-Flashback applies a simplified Graph Convolution Network (GCN) on the POI transition graph to enrich the representation of each POI. Further, we define a similarity function to consider both spatiotemporal information and user preference in modelling sequential regularity. Experimental results on two real-world datasets show that our proposed method achieves the state-of-the-art performance and significantly outperforms all existing solutions.

Supplemental Material

MP4 File
This is a presentation video about our work Graph-Flashback Network for Next Location Recommendation. In this paper, we construct a novel User-POI Knowledge Graph with strong representation ability, called Spatial-Temporal Knowledge Graph (STKG). STKG is used to learn the representations of each node (i.e., user, POI) and each edge. Then, we design a similarity function to construct our POI transition graph based on the learned representations. To incorporate the learned graph into sequential model, we propose a novel network Graph-Flashback for recommendation. Graph-Flashback applies a simplified Graph Convolution Network (GCN) on the POI transition graph to enrich the representation of each POI. Further, we define a similarity function to consider both spatiotemporal information and user preference in modelling sequential regularity. Experimental results on two real-world datasets show that our proposed method achieves the state-of-the-art performance and significantly outperforms all existing solutions.

References

[1]
Antoine Bordes, Nicolas Usunier, Alberto García-Durán, Jason Weston, and Oksana Yakhnenko. 2013. Translating Embeddings for Modeling Multi-relational Data. In NIPS.
[2]
Chen Cheng, Haiqin Yang, Michael R. Lyu, and Irwin King. 2013. Where You Like to Go Next: Successive Point-of-Interest Recommendation. In IJCAI.
[3]
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.
[4]
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.
[5]
Qing Guo, Zhu Sun, Jie Zhang, and Yin-Leng Theng. 2020. An Attentional Recurrent Neural Network for Personalized Next Location Recommendation. In AAAI.
[6]
Qingyu Guo, Fuzhen Zhuang, Chuan Qin, Hengshu Zhu, Xing Xie, Hui Xiong, and Qing He. 2020. A Survey on Knowledge Graph-Based Recommender Systems. CoRR (2020).
[7]
Peng Han, Zhongxiao Li, Yong Liu, Peilin Zhao, Jing Li, Hao Wang, and Shuo Shang. 2020. Contextualized Point-of-Interest Recommendation. In IJCAI.
[8]
Peng Han, Jin Wang, Di Yao, Shuo Shang, and Xiangliang Zhang. 2021. A Graph- based Approach for Trajectory Similarity Computation in Spatial Networks. In ACM SIGKDD. https://doi.org/10.1145/3447548.3467337
[9]
Xiangnan He, Kuan Deng, Xiang Wang, Yan Li, Yong-Dong Zhang, and Meng Wang. 2020. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. In SIGIR.
[10]
Dejiang Kong and Fei Wu. 2018. HST-LSTM: A Hierarchical Spatial-Temporal Long-Short Term Memory Network for Location Prediction. In IJCAI.
[11]
Yang Li, Tong Chen, Yadan Luo, Hongzhi Yin, and Zi Huang. 2021. Discovering Collaborative Signals for Next POI Recommendation with Iterative Seq2Graph Augmentation. In IJCAI.
[12]
Defu Lian, Yongji Wu, Yong Ge, Xing Xie, and Enhong Chen. 2020. Geography- Aware Sequential Location Recommendation. In SIGKDD.
[13]
Nicholas Lim, Bryan Hooi, See-Kiong Ng, Xueou Wang, Yong Liang Goh, Ren- rong Weng, and Jagannadan Varadarajan. 2020. STP-UDGAT: Spatial-Temporal- Preference User Dimensional Graph Attention Network for Next POI Recommendation. In CIKM.
[14]
Yankai Lin, Zhiyuan Liu, Maosong Sun, Yang Liu, and Xuan Zhu. 2015. Learning Entity and Relation Embeddings for Knowledge Graph Completion. In AAAI.
[15]
Qiang Liu, Shu Wu, Liang Wang, and Tieniu Tan. 2016. Predicting the Next Location: A Recurrent Model with Spatial and Temporal Contexts. In AAAI.
[16]
Xin Liu, Yong Liu, Karl Aberer, and Chunyan Miao. 2013. Personalized point-of- interest recommendation by mining users' preference transition. In CIKM.
[17]
Xin Liu, Yong Liu, and Xiaoli Li. 2016. Exploring the Context of Locations for Personalized Location Recommendations. In IJCAI.
[18]
Yong Liu, Wei Wei, Aixin Sun, and Chunyan Miao. 2014. Exploiting Geographical Neighborhood Characteristics for Location Recommendation. In CIKM.
[19]
Yong Liu, Lifan Zhao, Guimei Liu, Xinyan Lu, Peng Gao, Xiao-Li Li, and Zhihui Jin. 2018. Dynamic Bayesian Logistic Matrix Factorization for Recommendation with Implicit Feedback. In IJCAI.
[20]
Yingtao Luo, Qiang Liu, and Zhaocheng Liu. 2021. STAN: Spatio-Temporal Attention Network for Next Location Recommendation. In WWW.
[21]
Wesley Mathew, Ruben Raposo, and Bruno Martins. 2012. Predicting future locations with hidden Markov models. In UbiComp.
[22]
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.
[23]
Haining Tan, Di Yao, Tao Huang, Baoli Wang, Quanliang Jing, and Jingping Bi. 2021. Meta-Learning Enhanced Neural ODE for Citywide Next POI Recommendation. In MDM.
[24]
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is All you Need. In NIPS.
[25]
Hao Wang, Huawei Shen, Wentao Ouyang, and Xueqi Cheng. 2018. Exploiting POI-Specific Geographical Influence for Point-of-Interest Recommendation. In IJCAI.
[26]
Xiao Wang, Zheng Wang, Toshihiko Yamasaki, and Wenjun Zeng. 2021. Very Important Person Localization in Unconstrained Conditions: A New Benchmark. In AAAI.
[27]
Zhen Wang, Jianwen Zhang, Jianlin Feng, and Zheng Chen. 2014. Knowledge Graph Embedding by Translating on Hyperplanes. In AAAI.
[28]
Bishan Yang, Wen-tau Yih, Xiaodong He, Jianfeng Gao, and Li Deng. 2015. Embedding Entities and Relations for Learning and Inference in Knowledge Bases. In ICLR.
[29]
Dingqi Yang, Benjamin Fankhauser, Paolo Rosso, and Philippe Cudré-Mauroux. 2020. Location Prediction over Sparse User Mobility Traces Using RNNs: Flashback in Hidden States!. In IJCAI.
[30]
Dingqi Yang, Bingqing Qu, Jie Yang, and Philippe Cudré-Mauroux. 2019. Revisiting User Mobility and Social Relationships in LBSNs: A Hypergraph Embedding Approach. In WWW.
[31]
Di Yao, Chao Zhang, Jian-Hui Huang, and Jingping Bi. 2017. SERM: A Recurrent Model for Next Location Prediction in Semantic Trajectories. In CIKM.
[32]
Mao Ye, Peifeng Yin, Wang-Chien Lee, and Dik Lun Lee. 2011. Exploiting geo- graphical influence for collaborative point-of-interest recommendation. In SIGIR.
[33]
Fuqiang Yu, Lizhen Cui, Wei Guo, Xudong Lu, Qingzhong Li, and Hua Lu. 2020. A Category-Aware Deep Model for Successive POI Recommendation on Sparse Check-in Data. In WWW.
[34]
Zeping Yu, Jianxun Lian, Ahmad Mahmoody, Gongshen Liu, and Xing Xie. 2019. Adaptive User Modeling with Long and Short-Term Preferences for Personalized Recommendation. In IJCAI.
[35]
Yuyu Zhang, Hanjun Dai, Chang Xu, Jun Feng, Taifeng Wang, Jiang Bian, Bin Wang, and Tie-Yan Liu. 2014. Sequential Click Prediction for Sponsored Search with Recurrent Neural Networks. In AAAI.
[36]
Pengpeng Zhao, Haifeng Zhu, Yanchi Liu, Jiajie Xu, Zhixu Li, Fuzhen Zhuang, Victor S. Sheng, and Xiaofang Zhou. 2019. Where to Go Next: A Spatio-Temporal Gated Network for Next POI Recommendation. In AAAI.

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  • (2025)Heterogeneous Spatio-Temporal Graph Contrastive Learning for Point-of-Interest RecommendationTsinghua Science and Technology10.26599/TST.2023.901014830:1(186-197)Online publication date: Feb-2025
  • (2025)Next Point-of-Interest Recommendation With Adaptive Graph Contrastive LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.350948037:3(1366-1379)Online publication date: Mar-2025
  • (2025)Activity-Aware Human Mobility Prediction With Hierarchical Graph Attention Recurrent NetworkIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.351369526:2(1604-1616)Online publication date: Feb-2025
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cover image ACM Conferences
KDD '22: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
August 2022
5033 pages
ISBN:9781450393850
DOI:10.1145/3534678
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 14 August 2022

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Author Tags

  1. knowledge graph
  2. point-of-interest
  3. recommendation

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  • Research-article

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  • NSFC
  • STCSM
  • NKPs

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KDD '22
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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

View all
  • (2025)Heterogeneous Spatio-Temporal Graph Contrastive Learning for Point-of-Interest RecommendationTsinghua Science and Technology10.26599/TST.2023.901014830:1(186-197)Online publication date: Feb-2025
  • (2025)Next Point-of-Interest Recommendation With Adaptive Graph Contrastive LearningIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2024.350948037:3(1366-1379)Online publication date: Mar-2025
  • (2025)Activity-Aware Human Mobility Prediction With Hierarchical Graph Attention Recurrent NetworkIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2024.351369526:2(1604-1616)Online publication date: Feb-2025
  • (2025)Hypergraph User Embeddings and Session Contrastive Learning for POI RecommendationIEEE Access10.1109/ACCESS.2025.353139413(17983-17995)Online publication date: 2025
  • (2025)GeM: Gaussian embeddings with Multi-hop graph transfer for next POI recommendationNeural Networks10.1016/j.neunet.2025.107290(107290)Online publication date: Feb-2025
  • (2025)A user preference knowledge graph incorporating spatio-temporal transfer features for next POI recommendationApplied Intelligence10.1007/s10489-025-06290-y55:6Online publication date: 1-Apr-2025
  • (2025)GENET: Unleashing the Power of Side Information for Recommendation via Hypergraph Pre-trainingDatabase Systems for Advanced Applications10.1007/978-981-97-5555-4_24(343-352)Online publication date: 12-Jan-2025
  • (2024)Privacy Preserving Publish/Subscribe for Geo-Textual Data StreamsData Intelligence10.3724/2096-7004.di.2024.0014Online publication date: 17-Oct-2024
  • (2024)Effective Tool Augmented Multi-Agent Framework for Data AnalysisData Intelligence10.3724/2096-7004.di.2024.0013Online publication date: 17-Oct-2024
  • (2024)Parking Lot Traffic Prediction Based on Fusion of Multifaceted Spatio-Temporal FeaturesSensors10.3390/s2415497124:15(4971)Online publication date: 31-Jul-2024
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