Personalized Point-of-Interest Recommendation Using Improved Graph Convolutional Network in Location-Based Social Network
Data sparsity limits the performance of point-of-interest (POI) recommendation models, and the existing works ignore the higher-order collaborative influence of users and POIs and lack in-depth mining of user social influence, resulting in unsatisfactory recommendation results. To address the above...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Electronics |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2079-9292/12/16/3495 |
| _version_ | 1797584904274313216 |
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| author | Jingtong Liu Huawei Yi Yixuan Gao Rong Jing |
| author_facet | Jingtong Liu Huawei Yi Yixuan Gao Rong Jing |
| author_sort | Jingtong Liu |
| collection | DOAJ |
| description | Data sparsity limits the performance of point-of-interest (POI) recommendation models, and the existing works ignore the higher-order collaborative influence of users and POIs and lack in-depth mining of user social influence, resulting in unsatisfactory recommendation results. To address the above issues, this paper proposes a personalized POI recommendation using an improved graph convolutional network (PPR_IGCN) model, which integrates collaborative influence and social influence into POI recommendations. On the one hand, a user-POI interaction graph, a POI-POI graph, and a user–user graph are constructed based on check-in data and social data in a location-based social network (LBSN). The improved graph convolutional network (GCN) is used to mine the higher-order collaborative influence of users and POIs in the three types of relationship graphs and to deeply extract the potential features of users and POIs. On the other hand, the social influence of the user’s higher-order social friends and community neighbors on the user is obtained according to the user’s higher-order social embedding vector learned in the user–user graph. Finally, the captured user and POI’s higher-order collaborative influence and social influence are used to predict user preferences. The experimental results on Foursquare and Yelp datasets indicate that the proposed model PPR_IGCN outperforms other models in terms of precision, recall, and normalized discounted cumulative gain (NDCG), which proves the effectiveness of the model. |
| first_indexed | 2024-03-10T23:59:18Z |
| format | Article |
| id | doaj.art-813cbc2893204655bd4549f440f1e822 |
| institution | Directory Open Access Journal |
| issn | 2079-9292 |
| language | English |
| last_indexed | 2024-03-10T23:59:18Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Electronics |
| spelling | doaj.art-813cbc2893204655bd4549f440f1e8222023-11-19T00:54:30ZengMDPI AGElectronics2079-92922023-08-011216349510.3390/electronics12163495Personalized Point-of-Interest Recommendation Using Improved Graph Convolutional Network in Location-Based Social NetworkJingtong Liu0Huawei Yi1Yixuan Gao2Rong Jing3School of Electronics and Information Engineering, Liaoning University of Technology, Jinzhou 121001, ChinaSchool of Electronics and Information Engineering, Liaoning University of Technology, Jinzhou 121001, ChinaSchool of Electronics and Information Engineering, Liaoning University of Technology, Jinzhou 121001, ChinaSchool of Information Science and Engineering, Yanshan University, Qinhuangdao 066004, ChinaData sparsity limits the performance of point-of-interest (POI) recommendation models, and the existing works ignore the higher-order collaborative influence of users and POIs and lack in-depth mining of user social influence, resulting in unsatisfactory recommendation results. To address the above issues, this paper proposes a personalized POI recommendation using an improved graph convolutional network (PPR_IGCN) model, which integrates collaborative influence and social influence into POI recommendations. On the one hand, a user-POI interaction graph, a POI-POI graph, and a user–user graph are constructed based on check-in data and social data in a location-based social network (LBSN). The improved graph convolutional network (GCN) is used to mine the higher-order collaborative influence of users and POIs in the three types of relationship graphs and to deeply extract the potential features of users and POIs. On the other hand, the social influence of the user’s higher-order social friends and community neighbors on the user is obtained according to the user’s higher-order social embedding vector learned in the user–user graph. Finally, the captured user and POI’s higher-order collaborative influence and social influence are used to predict user preferences. The experimental results on Foursquare and Yelp datasets indicate that the proposed model PPR_IGCN outperforms other models in terms of precision, recall, and normalized discounted cumulative gain (NDCG), which proves the effectiveness of the model.https://www.mdpi.com/2079-9292/12/16/3495POI recommendationlocation social networkdata sparsitygraph convolutional networksocial influence |
| spellingShingle | Jingtong Liu Huawei Yi Yixuan Gao Rong Jing Personalized Point-of-Interest Recommendation Using Improved Graph Convolutional Network in Location-Based Social Network Electronics POI recommendation location social network data sparsity graph convolutional network social influence |
| title | Personalized Point-of-Interest Recommendation Using Improved Graph Convolutional Network in Location-Based Social Network |
| title_full | Personalized Point-of-Interest Recommendation Using Improved Graph Convolutional Network in Location-Based Social Network |
| title_fullStr | Personalized Point-of-Interest Recommendation Using Improved Graph Convolutional Network in Location-Based Social Network |
| title_full_unstemmed | Personalized Point-of-Interest Recommendation Using Improved Graph Convolutional Network in Location-Based Social Network |
| title_short | Personalized Point-of-Interest Recommendation Using Improved Graph Convolutional Network in Location-Based Social Network |
| title_sort | personalized point of interest recommendation using improved graph convolutional network in location based social network |
| topic | POI recommendation location social network data sparsity graph convolutional network social influence |
| url | https://www.mdpi.com/2079-9292/12/16/3495 |
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