# Personalized Point-of-Interest Recommendation Using Improved Graph Convolutional Network in Location-Based Social Network

^{1}

^{2}

^{*}

## Abstract

**:**

## 1. Introduction

- Apply the improved GCN to POI recommendation, construct a user-POI interaction graph, a POI-POI graph, and a user–user graph based on user history check-in data, deeply mine collaborative information in three types of relationships, and learn user and POI higher-order collaborative embedding vectors to improve recommendation performance;
- Based on the users’ higher-order social embedding vector learned in the user–user graph, the higher-order social friend influence of the user and social friends and the community neighbor influence of the user and close neighbors are captured to alleviate data sparsity;
- Conduct experiments on real datasets to evaluate the performance of the proposed model, PPR_IGCN. The experimental results show that the model outperforms other existing models, which verifies the effectiveness of the proposed model.

## 2. Related Work

#### 2.1. POI Recommendation

#### 2.2. Recommendation Based on GCN

## 3. Proposed Model

#### 3.1. Preliminaries

#### 3.2. PPR_IGCN Model

#### 3.3. The Learning Mode of Multi-Dimensional Collaborative Influence

#### 3.3.1. Learning the Collaborative Influence of the User-POI Interaction Graph

#### 3.3.2. Learning the Collaborative Influence of the POI-POI Graph

#### 3.3.3. Learning the Collaborative Influence of the User–User Graph

#### 3.4. Learning Social Influence

#### 3.5. Model Prediction and Optimization

## 4. Experiments

#### 4.1. Datasets

#### 4.2. Evaluation Metrics

#### 4.3. Parameter Settings

- (1)
- Determination of user and POI embedding vector dimension d

- (2)
- Determination of the number n of the most similar locations/friends/neighbors

#### 4.4. Compared Experiment

- (1)
- LightGCN [28]: This model removes unnecessary feature transformation and nonlinear activation in traditional GCNs, simplifying the design of neighborhood aggregation in GCN;
- (2)
- FGRec [34]: A fine-grained POI recommendation framework is proposed; a group friend model is designed to capture social influence; a joint Poisson factor model is used to learn category influence; and the personalized Gaussian kernel model is used to capture geographic information influence;
- (3)
- LGLMF [37]: A method for implementing POI recommendations by integrating the local geographic model into the logistic MF algorithm;
- (4)
- FGCRec [38]: A unified probability distribution model based on four key geographic features that captures geographic influence from the perspectives of users and locations and explores the contribution of check-in frequency;
- (5)
- SUCP [39]: A social user activity center POI recommendation system, which jointly models the user activity center and social relationships based on CF.

#### 4.5. Performance Comparison and Analysis

#### 4.6. Ablation Study

## 5. Conclusions

## Author Contributions

## Funding

## Data Availability Statement

## Conflicts of Interest

## References

- Hashim-Jones, J.; Wang, C.; Islam, M.S.; Stantic, B. Interdependent Model for Point-of-Interest Recommendation via Social Networks; Springer International Publishing: Cham, Switzerland, 2018; pp. 161–173. [Google Scholar]
- Papangelis, K.; Chamberlain, A.; Lykourentzou, I.; Khan, V.-J.; Saker, M.; Liang, H.-N.; Sadien, I.; Cao, T. Performing the Digital Self: Understanding Location-Based Social Networking, Territory, Space, and Identity in the City. ACM Trans. Comput.-Hum. Interact.
**2020**, 27, 1–26. [Google Scholar] [CrossRef] - Chakraborty, A.; Ganguly, D.; Caputo, A.; Jones, G.J.F. Kernel Density Estimation Based Factored Relevance Model for Multi-Contextual Point-of-Interest Recommendation. Inf. Retr.
**2022**, 25, 44–90. [Google Scholar] [CrossRef] - Han, P.; Shang, S.; Sun, A.; Zhao, P.; Zheng, K.; Zhang, X. Point-of-Interest Recommendation With Global and Local Context. IEEE Trans. Knowl. Data Eng.
**2022**, 34, 5484–5495. [Google Scholar] [CrossRef] - Chang, W.; Sun, D.; Du, Q. Intelligent Sensors for POI Recommendation Model Using Deep Learning in Location-Based Social Network Big Data. Sensors
**2023**, 23, 850. [Google Scholar] [CrossRef] - Li, J.; Cai, T.; Mian, A.; Li, R.H.; Sellis, T.; Yu, J.X. Holistic Influence Maximization for Targeted Advertisements in Spatial Social Networks. In Proceedings of the 2018 IEEE 34th International Conference on Data Engineering (ICDE), Paris, France, 16–19 April 2018; pp. 1340–1343. [Google Scholar]
- Yin, H.; Wang, W.; Wang, H.; Chen, L.; Zhou, X. Spatial-Aware Hierarchical Collaborative Deep Learning for POI Recommendation. IEEE Trans. Knowl. Data Eng.
**2017**, 29, 2537–2551. [Google Scholar] [CrossRef] - Qian, T.; Liu, B.; Nguyen, Q.V.H.; Yin, H. Spatiotemporal Representation Learning for Translation-Based POI Recommendation. ACM Trans. Inf. Syst.
**2019**, 37, 1–24. [Google Scholar] [CrossRef] - Wang, W.; Chen, J.; Wang, J.; Chen, J.; Liu, J.; Gong, Z. Trust-Enhanced Collaborative Filtering for Personalized Point of Interests Recommendation. IEEE Trans. Ind. Inform.
**2020**, 16, 6124–6132. [Google Scholar] [CrossRef] - Xu, C.; Ding, A.S.; Zhao, K. A novel POI recommendation method based on trust relationship and spatial–temporal factors. Electron. Commer. Res. Appl.
**2021**, 48, 101060. [Google Scholar] [CrossRef] - Zhou, F.; Qian, T.; Mo, Y.; Cheng, Z.; Xiao, C.; Wu, J.; Trajcevski, G. Uncertainty-Aware Heterogeneous Representation Learning in POI Recommender Systems. IEEE Trans. Syst. Man Cybern. Syst.
**2023**, 53, 4522–4535. [Google Scholar] [CrossRef] - Fang, J.; Meng, X.; Qi, X. A Top-k POI Recommendation Approach Based on LBSN and Multi-Graph Fusion. Neurocomputing
**2023**, 518, 219–230. [Google Scholar] [CrossRef] - Kipf, T.N.; Welling, M. Semi-Supervised Classification with Graph Convolutional Networks. arXiv
**2016**, arXiv:1609.02907. [Google Scholar] [CrossRef] - Wang, H.; Zhao, M.; Xie, X.; Li, W.; Guo, M. Knowledge Graph Convolutional Networks for Recommender Systems. In Proceedings of the WWW ’19: The World Wide Web Conference, San Francisco, CA, USA, 13–17 May 2019; pp. 3307–3313. [Google Scholar] [CrossRef]
- Wang, X.; He, X.; Cao, Y.; Liu, M.; Chua, T.-S. KGAT: Knowledge Graph Attention Network for Recommendation. arXiv
**2019**, arXiv:1905.07854. [Google Scholar] [CrossRef] - Sun, J.; Zhang, Y.; Guo, W.; Guo, H.; Tang, R.; He, X.; Ma, C.; Coates, M. Neighbor Interaction Aware Graph Convolution Networks for Recommendation. In Proceedings of the SIGIR ‘20: The 43rd International ACM SIGIR conference on research and development in Information Retrieval, Virtual Event, China, 25–30 July 2020; pp. 1289–1298. [Google Scholar]
- Chen, J.; Zhang, W. Review of point of interest recommendation systems based on location-based social networks. J. Front. Comput. Sci. Technol.
**2022**, 16, 1462–1478. [Google Scholar] - Li, H.; Ge, Y.; Hong, R.; Zhu, H. Point-of-Interest Recommendations: Learning Potential Check-Ins from Friends. In Proceedings of the KDD ‘16: The 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, San Francisco, CA, USA, 13–17 August 2016; pp. 975–984. [Google Scholar]
- Gao, H.; Tang, J.; Hu, X.; Liu, H. Exploring Temporal Effects for Location Recommendation on Location-Based Social Networks. In Proceedings of the RecSys ‘13: Seventh ACM Conference on Recommender Systems, Hong Kong, China, 12–16 October 2013; pp. 93–100. [Google Scholar]
- Liu, B.; Xiong, H.; Papadimitriou, S.; Fu, Y.; Yao, Z. A General Geographical Probabilistic Factor Model for Point of Interest Recommendation. IEEE Trans. Knowl. Data Eng.
**2015**, 27, 1167–1179. [Google Scholar] [CrossRef] - Wang, X.; Salim, F.D.; Ren, Y.; Koniusz, P. Relation Embedding for Personalised POI Recommendation. arXiv
**2020**, arXiv:2002.03461. [Google Scholar] [CrossRef] - Xu, Y.; Li, X.; Li, J.; Wang, C.; Gao, R.; Yu, Y. SSSER: Spatiotemporal Sequential and Social Embedding Rank for Successive Point-of-Interest Recommendation. IEEE Access
**2019**, 7, 156804–156823. [Google Scholar] [CrossRef] - Zhu, J.; Guo, X. Deep Neural Model for Point-of-Interest Recommendation Fused with Graph Embedding Representation. In Proceedings of the Wireless Algorithms, Systems, and Applications, Honolulu, HI, USA, 24–26 June 2019; pp. 495–506. [Google Scholar]
- Cao, K.; Guo, J.; Meng, G.; Liu, H.; Liu, Y.; Li, G. Points-of-Interest Recommendation Algorithm Based on LBSN in Edge Computing Environment. IEEE Access
**2020**, 8, 47973–47983. [Google Scholar] [CrossRef] - Lian, D.; Zhao, C.; Xie, X.; Sun, G.; Chen, E.; Rui, Y. GeoMF: Joint Geographical Modeling and Matrix Factorization for Point-of-Interest Recommendation. In Proceedings of the KDD ‘14: The 20th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, New York, NY, USA, 24–27 August 2014; pp. 831–840. [Google Scholar]
- Wang, X.; He, X.; Wang, M.; Feng, F.; Chua, T.-S. Neural Graph Collaborative Filtering. arXiv
**2019**, arXiv:1905.08108. [Google Scholar] [CrossRef] - Wu, S.; Zhang, Y.; Gao, C.; Bian, K.; Cui, B. GARG: Anonymous Recommendation of Point-of-Interest in Mobile Networks by Graph Convolution Network. Data Sci. Eng.
**2020**, 5, 433–447. [Google Scholar] [CrossRef] - He, X.; Deng, K.; Wang, X.; Li, Y.; Zhang, Y.; Wang, M. LightGCN: Simplifying and Powering Graph Convolution Network for Recommendation. arXiv
**2020**, arXiv:2002.02126. [Google Scholar] [CrossRef] - Chang, B.; Jang, G.; Kim, S.; Kang, J. Learning Graph-Based Geographical Latent Representation for Point-of-Interest Recommendation. In Proceedings of the CIKM ‘20: The 29th ACM International Conference on Information and Knowledge Management, Virtual Event, Ireland, 19–23 October 2020; pp. 135–144. [Google Scholar]
- Li, J.; Wang, X.; Feng, W. A Point-of-Interest Recommendation Algorithm Combining Social Influence and Geographic Location Based on Belief Propagation. IEEE Access
**2020**, 8, 165748–165756. [Google Scholar] [CrossRef] - Zhong, T.; Zhang, S.; Zhou, F.; Zhang, K.; Trajcevski, G.; Wu, J. Hybrid graph convolutional networks with multi-head attention for location recommendation. World Wide Web
**2020**, 23, 3125–3151. [Google Scholar] [CrossRef] - Ying, R.; He, R.; Chen, K.; Eksombatchai, P.; Hamilton, W.L.; Leskovec, J. Graph Convolutional Neural Networks for Web-Scale Recommender Systems. arXiv
**2018**, arXiv:1806.01973. [Google Scholar] - Mikolov, T.; Sutskever, I.; Chen, K.; Corrado, G.; Dean, J. Distributed Representations of Words and Phrases and Their Compositionality. In Proceedings of the Nips’13: The 26th International Conference on Neural Information Processing Systems, Lake Tahoe, NV, USA, 5–10 December 2013; pp. 3111–3119. [Google Scholar]
- Su, Y.; Zhang, J.D.; Li, X.; Zha, D.; Xiang, J.; Tang, W.; Gao, N. FGRec: A Fine-Grained Point-of-Interest Recommendation Framework by Capturing Intrinsic Influences. In Proceedings of the 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, 19–24 July 2020; pp. 1–9. [Google Scholar]
- Ma, C.; Zhang, Y.; Wang, Q.; Liu, X. Point-of-Interest Recommendation: Exploiting Self-Attentive Autoencoders with Neighbor-Aware Influence. In Proceedings of the CIKM ‘18: The 27th ACM International Conference on Information and Knowledge Management, Torino, Italy, 22–26 October 2018; pp. 697–706. [Google Scholar]
- Toh, S.C.; Lai, S.H.; Mirzaei, M.; Soo, E.Z.X.; Teo, F.Y. Sequential Data Processing for IMERG Satellite Rainfall Comparison and Improvement Using LSTM and ADAM Optimizer. Appl. Sci.
**2023**, 13, 7237. [Google Scholar] [CrossRef] - Rahmani, H.A.; Aliannejadi, M.; Ahmadian, S.; Baratchi, M.; Afsharchi, M.; Crestani, F. LGLMF: Local Geographical Based Logistic Matrix Factorization Model for POI Recommendation. In Proceedings of the Information Retrieval Technology, Hong Kong, China, 7–9 November 2019; pp. 66–78. [Google Scholar]
- Su, Y.; Li, X.; Liu, B.; Zha, D.; Xiang, J.; Tang, W.; Gao, N. FGCRec: Fine-Grained Geographical Characteristics Modeling for Point-of-Interest Recommendation. In Proceedings of the ICC 2020–2020 IEEE International Conference on Communications (ICC), Dublin, Ireland, 7–11 June 2020; pp. 1–6. [Google Scholar]
- Seyedhoseinzadeh, K.; Rahmani, H.A.; Afsharchi, M.; Aliannejadi, M. Leveraging Social Influence Based on Users Activity Centers for Point-of-Interest Recommendation. Inf. Process. Manag.
**2022**, 59, 102858. [Google Scholar] [CrossRef]

**Figure 3.**First-order connectivity versus higher-order connectivity. (

**a**) User-POI interaction graph; (

**b**) Higher-order neighbor graph for ${u}_{1}$.

Dataset | Number of Users | Number of POIs | Number of Check-Ins | Number of Social Links | Density |
---|---|---|---|---|---|

Foursquare | 30,887 | 18,995 | 860,888 | 265,533 | 0.14% |

Yelp | 2551 | 13,474 | 124,933 | 32,512 | 0.29% |

Parameter | Meaning | Value |
---|---|---|

$lr$ | Learning rate | ${10}^{-3}$ |

$d$ | The dimension of user and POI embedding vectors | 192 |

$w$ | Calculate the weight coefficient of similarity between the users and friends | $0.5$ |

$\alpha $, $\beta $ | Coefficients that control the weight of collaborative and social influence when computing user preferences | 1.1, 1 |

$n$ | The number of the most similar locations/friends/neighbors | 10 |

${w}_{1}$, ${w}_{2}$, ${w}_{3}$ | Hyperparameters that control the importance of user-POI relation, POI-POI relation, and user–user relation | 1, 0.01, 0.01 |

$\lambda $ | L2 regularization parameter | ${10}^{-4}$ |

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |

© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Liu, J.; Yi, H.; Gao, Y.; Jing, R.
Personalized Point-of-Interest Recommendation Using Improved Graph Convolutional Network in Location-Based Social Network. *Electronics* **2023**, *12*, 3495.
https://doi.org/10.3390/electronics12163495

**AMA Style**

Liu J, Yi H, Gao Y, Jing R.
Personalized Point-of-Interest Recommendation Using Improved Graph Convolutional Network in Location-Based Social Network. *Electronics*. 2023; 12(16):3495.
https://doi.org/10.3390/electronics12163495

**Chicago/Turabian Style**

Liu, Jingtong, Huawei Yi, Yixuan Gao, and Rong Jing.
2023. "Personalized Point-of-Interest Recommendation Using Improved Graph Convolutional Network in Location-Based Social Network" *Electronics* 12, no. 16: 3495.
https://doi.org/10.3390/electronics12163495