Exploring an Efficient POI Recommendation Model Based on User Characteristics and Spatial-Temporal Factors
The advent of mobile scenario-based consumption popularizes and gradually maturates the application of point of interest (POI) recommendation services based on geographical location. However, the insufficient fusion of heterogeneous data in the current POI recommendation services leads to poor recom...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-10-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/9/21/2673 |
| _version_ | 1797512103939014656 |
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| author | Chonghuan Xu Dongsheng Liu Xinyao Mei |
| author_facet | Chonghuan Xu Dongsheng Liu Xinyao Mei |
| author_sort | Chonghuan Xu |
| collection | DOAJ |
| description | The advent of mobile scenario-based consumption popularizes and gradually maturates the application of point of interest (POI) recommendation services based on geographical location. However, the insufficient fusion of heterogeneous data in the current POI recommendation services leads to poor recommendation quality. In this paper, we propose a novel hybrid POI recommendation model (NHRM) based on user characteristics and spatial-temporal factors to enhance the recommendation effect. The proposed model contains three sub-models. The first model considers user preferences, forgetting characteristics, user influence, and trajectories. The second model studies the impact of the correlation between the locations of POIs and calculates the check-in probability of POI with the two-dimensional kernel density estimation method. The third model analyzes the influence of category of POI. Consequently, the above results were combined and top-<i>K</i> POIs were recommended to target users. The experimental results on Yelp and Meituan data sets showed that the recommendation performance of our method is superior to some other methods, and the problems of cold-start and data sparsity are alleviated to a certain extent. |
| first_indexed | 2024-03-10T05:57:09Z |
| format | Article |
| id | doaj.art-c1bcae0aaa9f4be7858a09b80c8519e3 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T05:57:09Z |
| publishDate | 2021-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-c1bcae0aaa9f4be7858a09b80c8519e32023-11-22T21:17:10ZengMDPI AGMathematics2227-73902021-10-01921267310.3390/math9212673Exploring an Efficient POI Recommendation Model Based on User Characteristics and Spatial-Temporal FactorsChonghuan Xu0Dongsheng Liu1Xinyao Mei2School of Business Administration, Zhejiang Gongshang University, Hangzhou 310018, ChinaSchool of Computer Science and Information Engineering, Zhejiang Gongshang University, Hangzhou 310018, ChinaSchool of Management and E-Business, Zhejiang Gongshang University, Hangzhou 310018, ChinaThe advent of mobile scenario-based consumption popularizes and gradually maturates the application of point of interest (POI) recommendation services based on geographical location. However, the insufficient fusion of heterogeneous data in the current POI recommendation services leads to poor recommendation quality. In this paper, we propose a novel hybrid POI recommendation model (NHRM) based on user characteristics and spatial-temporal factors to enhance the recommendation effect. The proposed model contains three sub-models. The first model considers user preferences, forgetting characteristics, user influence, and trajectories. The second model studies the impact of the correlation between the locations of POIs and calculates the check-in probability of POI with the two-dimensional kernel density estimation method. The third model analyzes the influence of category of POI. Consequently, the above results were combined and top-<i>K</i> POIs were recommended to target users. The experimental results on Yelp and Meituan data sets showed that the recommendation performance of our method is superior to some other methods, and the problems of cold-start and data sparsity are alleviated to a certain extent.https://www.mdpi.com/2227-7390/9/21/2673POI recommendationuser preferenceuser influenceforgetting characteristictrajectory |
| spellingShingle | Chonghuan Xu Dongsheng Liu Xinyao Mei Exploring an Efficient POI Recommendation Model Based on User Characteristics and Spatial-Temporal Factors Mathematics POI recommendation user preference user influence forgetting characteristic trajectory |
| title | Exploring an Efficient POI Recommendation Model Based on User Characteristics and Spatial-Temporal Factors |
| title_full | Exploring an Efficient POI Recommendation Model Based on User Characteristics and Spatial-Temporal Factors |
| title_fullStr | Exploring an Efficient POI Recommendation Model Based on User Characteristics and Spatial-Temporal Factors |
| title_full_unstemmed | Exploring an Efficient POI Recommendation Model Based on User Characteristics and Spatial-Temporal Factors |
| title_short | Exploring an Efficient POI Recommendation Model Based on User Characteristics and Spatial-Temporal Factors |
| title_sort | exploring an efficient poi recommendation model based on user characteristics and spatial temporal factors |
| topic | POI recommendation user preference user influence forgetting characteristic trajectory |
| url | https://www.mdpi.com/2227-7390/9/21/2673 |
| work_keys_str_mv | AT chonghuanxu exploringanefficientpoirecommendationmodelbasedonusercharacteristicsandspatialtemporalfactors AT dongshengliu exploringanefficientpoirecommendationmodelbasedonusercharacteristicsandspatialtemporalfactors AT xinyaomei exploringanefficientpoirecommendationmodelbasedonusercharacteristicsandspatialtemporalfactors |