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dc.contributor.authorWu, Ziqingen_US
dc.description.abstractNext 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.en_US
dc.publisherNanyang Technological Universityen_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleNext point of interest (POI) recommendationen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.description.degreeBachelor of Engineering (Computer Science)en_US
dc.contributor.supervisor2Zhang Jieen_US
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Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)
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