Travel place recommendation system using K-Nearest Neighbors algorithm / Nur Amirah Shahidan
Nowadays, recommendation system is the most widely system people used. Recommendation system can help people to find the right items that match to their interest. Besides, recommendation system also popular in tourism industry. People are going to travel for many reasons. Sometimes, they are going t...
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| 格式: | Thesis |
| 语言: | English |
| 出版: |
2021
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| 主题: | |
| 在线阅读: | https://ir.uitm.edu.my/id/eprint/55325/1/55325.pdf |
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| 总结: | Nowadays, recommendation system is the most widely system people used. Recommendation system can help people to find the right items that match to their interest. Besides, recommendation system also popular in tourism industry. People are going to travel for many reasons. Sometimes, they are going to the destination that they are never been there. Traveller often confused about where they want to go since they are not familiar with the places. Because of that, traveller need something to recommend them some places they can go. Recommendation system can helps traveller when they are going to travel, reaching a new and unfamiliar places. The proposed system which is Travel Place Recommendation System using K-Nearest Neighbors can helps traveller to find they places they might be interested with. The Travel Place Recommendation System will calculate the similarity between user’s rating and compare each of the user to recommend several travel places to user. The method has been proposed in the recommendation system is K-Nearest Neighbors algorithm. K-Nearest Neighbors algorithm is widely used among classification algorithm in recommendation system. It will generate the recommended places by comparing the K items with the neighbors. The result from the proposed system is recommendation system will display five recommended travel places to user. As a conclusion, Travel Place Recommendation System has been successfully developed by using the attributes of the system which is place rating. The algorithm used in the recommendation system has been evaluated by using MAE, MSE and RMSE to calculate the accuracy of the algorithm. However, Travel Place Recommendation System consists several limitation that need to improve to make the recommendation system become successful and attract more users. |
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