Exploring Spatial and Mobility Pattern’s Effects for Collaborative Point-of-Interest Recommendation
In recent years, researches on the mining of user check-in behaviors for point-of-interest(POI) recommendations has attracted a lot of attention. Personalized POI recommendation is a significant task in location-based social networks(LBSNs) because it helps target users explore their surrounding env...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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IEEE
2019-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8889741/ |
| _version_ | 1818444361853042688 |
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| author | Xu Jiao Yingyuan Xiao Wenguang Zheng Lei Xu Hui Wu |
| author_facet | Xu Jiao Yingyuan Xiao Wenguang Zheng Lei Xu Hui Wu |
| author_sort | Xu Jiao |
| collection | DOAJ |
| description | In recent years, researches on the mining of user check-in behaviors for point-of-interest(POI) recommendations has attracted a lot of attention. Personalized POI recommendation is a significant task in location-based social networks(LBSNs) because it helps target users explore their surrounding environment and greatly benefits the business in real life. Although a personalized POI recommendation system can significantly facilitate users' outdoor activities, it faces many challenging problems, such as the hardness to model human mobility and the difficulty to address data sparsity. Moreover, geographical influence on users should be personalized, but current studies only model the geographical influence on all users' check-in behaviors in a universal way. In this paper, we design a novel and effective personalized POI recommendation system. First, our system mines the target user's active area based on his or her check-in history, and designs a personalized user spatial similarity calculation method based on the target user's active area. Secondly, our system takes into account three features of the human mobility pattern: spatial, temporal, and sequential properties. Furthermore, our system designs a novel personalized user mobility pattern similarity calculation method based on the features of human mobility pattern. Finally, a recommendation list is generated based on the idea of collaborative filtering. Compared with the state-of-the-art POI recommendation approaches, the experimental results demonstrate that our system achieves much better performance. |
| first_indexed | 2024-12-14T19:14:43Z |
| format | Article |
| id | doaj.art-5128e0e815ff429bb1efd30d7ef316d1 |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-14T19:14:43Z |
| publishDate | 2019-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-5128e0e815ff429bb1efd30d7ef316d12022-12-21T22:50:39ZengIEEEIEEE Access2169-35362019-01-01715891715893010.1109/ACCESS.2019.29509278889741Exploring Spatial and Mobility Pattern’s Effects for Collaborative Point-of-Interest RecommendationXu Jiao0Yingyuan Xiao1https://orcid.org/0000-0002-5711-8638Wenguang Zheng2Lei Xu3Hui Wu4Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin, ChinaTianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin, ChinaTianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin, ChinaEconomics and Management College, Civil Aviation University of China, Tianjin, ChinaSchool of Computer Science and Engineering, The University of New South Wales at Sydney, Sydney, NSW, AustraliaIn recent years, researches on the mining of user check-in behaviors for point-of-interest(POI) recommendations has attracted a lot of attention. Personalized POI recommendation is a significant task in location-based social networks(LBSNs) because it helps target users explore their surrounding environment and greatly benefits the business in real life. Although a personalized POI recommendation system can significantly facilitate users' outdoor activities, it faces many challenging problems, such as the hardness to model human mobility and the difficulty to address data sparsity. Moreover, geographical influence on users should be personalized, but current studies only model the geographical influence on all users' check-in behaviors in a universal way. In this paper, we design a novel and effective personalized POI recommendation system. First, our system mines the target user's active area based on his or her check-in history, and designs a personalized user spatial similarity calculation method based on the target user's active area. Secondly, our system takes into account three features of the human mobility pattern: spatial, temporal, and sequential properties. Furthermore, our system designs a novel personalized user mobility pattern similarity calculation method based on the features of human mobility pattern. Finally, a recommendation list is generated based on the idea of collaborative filtering. Compared with the state-of-the-art POI recommendation approaches, the experimental results demonstrate that our system achieves much better performance.https://ieeexplore.ieee.org/document/8889741/POI recommendationuser active areauser mobility patternthe minimum enclosing circle |
| spellingShingle | Xu Jiao Yingyuan Xiao Wenguang Zheng Lei Xu Hui Wu Exploring Spatial and Mobility Pattern’s Effects for Collaborative Point-of-Interest Recommendation IEEE Access POI recommendation user active area user mobility pattern the minimum enclosing circle |
| title | Exploring Spatial and Mobility Pattern’s Effects for Collaborative Point-of-Interest Recommendation |
| title_full | Exploring Spatial and Mobility Pattern’s Effects for Collaborative Point-of-Interest Recommendation |
| title_fullStr | Exploring Spatial and Mobility Pattern’s Effects for Collaborative Point-of-Interest Recommendation |
| title_full_unstemmed | Exploring Spatial and Mobility Pattern’s Effects for Collaborative Point-of-Interest Recommendation |
| title_short | Exploring Spatial and Mobility Pattern’s Effects for Collaborative Point-of-Interest Recommendation |
| title_sort | exploring spatial and mobility pattern x2019 s effects for collaborative point of interest recommendation |
| topic | POI recommendation user active area user mobility pattern the minimum enclosing circle |
| url | https://ieeexplore.ieee.org/document/8889741/ |
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