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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| 格式: | Final Year Project |
| 語言: | English |
| 出版: |
Nanyang Technological University
2020
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/138864 |
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| 機構: | Nanyang Technological University |
| 語言: | English |
| 總結: | 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. |
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