Next point-of-interest recommendation
In the recent years, Next Point-of-Interest (POI) recommendation system has become more popular. The goal of POI recommendation system is to give POI recommendation to users given the users' historical check-in history. It is important to take into account the users recent check-in sequence and...
محفوظ في:
المؤلف الرئيسي: | |
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مؤلفون آخرون: | |
التنسيق: | Final Year Project |
اللغة: | English |
منشور في: |
Nanyang Technological University
2022
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الموضوعات: | |
الوصول للمادة أونلاين: | https://hdl.handle.net/10356/156514 |
الوسوم: |
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المؤسسة: | Nanyang Technological University |
اللغة: | English |
الملخص: | In the recent years, Next Point-of-Interest (POI) recommendation system has become more popular. The goal of POI recommendation system is to give POI recommendation to users given the users' historical check-in history. It is important to take into account the users recent check-in sequence and their preference to give accurate recommendations. This report proposes a POI recommendation model that utilizes multi-task learning that considers both the long-term preference and short-term preference of the users. The long-term component will learn about the user preference, and the short-term component will learn about the recent sequential check-in. The performance of the proposed model will then be compared to other baseline models to highlight the advantages of the proposed model. |
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