Next point of interest (POI) recommendation
Next Point-of-Interest (POI) Recommendation systems nowadays often assume that the users' check-in records are accurate. However, the accuracy and certainty of a user's check-in history may not be guaranteed in a real-world application due to various reasons. In order to make POI Recommend...
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| Format: | Final Year Project (FYP) |
| Language: | English |
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Nanyang Technological University
2020
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| Online Access: | https://hdl.handle.net/10356/138864 |
| _version_ | 1826113430066561024 |
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| author | Wu, Ziqing |
| author2 | - |
| author_facet | - Wu, Ziqing |
| author_sort | Wu, Ziqing |
| collection | NTU |
| description | Next Point-of-Interest (POI) Recommendation systems nowadays often assume that the users' check-in records are accurate. However, the accuracy and certainty of a user's check-in history may not be guaranteed in a real-world application due to various reasons. In order to make POI Recommendation systems overcome this problem, we first processed and analyzed real-world data to investigate the key
influencer of users' decisions. This report then proposes a novel model that utilizes the users' past spatial and temporal information to predict users' intentions and offer them suggestions on where to go. Experiments were conducted on 3 sets of real-world data. The performance of the model was also compared with other baseline models to demonstrate the advantages of this model. |
| first_indexed | 2024-10-01T03:23:06Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/138864 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:23:06Z |
| publishDate | 2020 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1388642020-05-13T07:15:04Z Next point of interest (POI) recommendation Wu, Ziqing - School of Computer Science and Engineering Zhang Jie ZhangJ@ntu.edu.sg Engineering::Computer science and engineering Next Point-of-Interest (POI) Recommendation systems nowadays often assume that the users' check-in records are accurate. However, the accuracy and certainty of a user's check-in history may not be guaranteed in a real-world application due to various reasons. In order to make POI Recommendation systems overcome this problem, we first processed and analyzed real-world data to investigate the key influencer of users' decisions. This report then proposes a novel model that utilizes the users' past spatial and temporal information to predict users' intentions and offer them suggestions on where to go. Experiments were conducted on 3 sets of real-world data. The performance of the model was also compared with other baseline models to demonstrate the advantages of this model. Bachelor of Engineering (Computer Science) 2020-05-13T07:15:04Z 2020-05-13T07:15:04Z 2020 Final Year Project (FYP) https://hdl.handle.net/10356/138864 en SCSE19-0014 application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Computer science and engineering Wu, Ziqing Next point of interest (POI) recommendation |
| title | Next point of interest (POI) recommendation |
| title_full | Next point of interest (POI) recommendation |
| title_fullStr | Next point of interest (POI) recommendation |
| title_full_unstemmed | Next point of interest (POI) recommendation |
| title_short | Next point of interest (POI) recommendation |
| title_sort | next point of interest poi recommendation |
| topic | Engineering::Computer science and engineering |
| url | https://hdl.handle.net/10356/138864 |
| work_keys_str_mv | AT wuziqing nextpointofinterestpoirecommendation |