Relational POI recommendation model combined with geographic information.
Point of interest (POI) recommendation is a popular personalized location-based service. This paper proposes a Geographic Personal Matrix Factorization (GPMF) model that makes effective use of geographic information from the perspective of the relationship between POIs and users. This model consider...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Public Library of Science (PLoS)
2022-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0266340 |
| _version_ | 1828326197618016256 |
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| author | Ke Li Haitao Wei Xiaohui He Zhihui Tian |
| author_facet | Ke Li Haitao Wei Xiaohui He Zhihui Tian |
| author_sort | Ke Li |
| collection | DOAJ |
| description | Point of interest (POI) recommendation is a popular personalized location-based service. This paper proposes a Geographic Personal Matrix Factorization (GPMF) model that makes effective use of geographic information from the perspective of the relationship between POIs and users. This model considers the role of geographic information from multiple perspectives based on the locational relationship among users, the distributional relationship between users and POIs, and the proximity and clustering relationship among POIs. The GPMF mines the influence of geographic information on different objects and carries out unique modeling through cosine similarity, non-linear function, and k nearest neighbor (KNN). This study explored the influence of geographic information on POI recommendation through extensive experiments with data from Foursquare. The result shows that GPMF performs better than the commonly used POI recommendation algorithm in terms of both precision and recall. Geographic information through proximity relations effectively improves the recommendation algorithm. |
| first_indexed | 2024-04-13T19:35:26Z |
| format | Article |
| id | doaj.art-626804120d0e41f88ba78b772f2435ab |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-04-13T19:35:26Z |
| publishDate | 2022-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-626804120d0e41f88ba78b772f2435ab2022-12-22T02:33:03ZengPublic Library of Science (PLoS)PLoS ONE1932-62032022-01-01174e026634010.1371/journal.pone.0266340Relational POI recommendation model combined with geographic information.Ke LiHaitao WeiXiaohui HeZhihui TianPoint of interest (POI) recommendation is a popular personalized location-based service. This paper proposes a Geographic Personal Matrix Factorization (GPMF) model that makes effective use of geographic information from the perspective of the relationship between POIs and users. This model considers the role of geographic information from multiple perspectives based on the locational relationship among users, the distributional relationship between users and POIs, and the proximity and clustering relationship among POIs. The GPMF mines the influence of geographic information on different objects and carries out unique modeling through cosine similarity, non-linear function, and k nearest neighbor (KNN). This study explored the influence of geographic information on POI recommendation through extensive experiments with data from Foursquare. The result shows that GPMF performs better than the commonly used POI recommendation algorithm in terms of both precision and recall. Geographic information through proximity relations effectively improves the recommendation algorithm.https://doi.org/10.1371/journal.pone.0266340 |
| spellingShingle | Ke Li Haitao Wei Xiaohui He Zhihui Tian Relational POI recommendation model combined with geographic information. PLoS ONE |
| title | Relational POI recommendation model combined with geographic information. |
| title_full | Relational POI recommendation model combined with geographic information. |
| title_fullStr | Relational POI recommendation model combined with geographic information. |
| title_full_unstemmed | Relational POI recommendation model combined with geographic information. |
| title_short | Relational POI recommendation model combined with geographic information. |
| title_sort | relational poi recommendation model combined with geographic information |
| url | https://doi.org/10.1371/journal.pone.0266340 |
| work_keys_str_mv | AT keli relationalpoirecommendationmodelcombinedwithgeographicinformation AT haitaowei relationalpoirecommendationmodelcombinedwithgeographicinformation AT xiaohuihe relationalpoirecommendationmodelcombinedwithgeographicinformation AT zhihuitian relationalpoirecommendationmodelcombinedwithgeographicinformation |