Machine learning for new friends recommendation in NTU
NiceTomeetU (NTmU) is a friend recommendation platform exclusive to Nanyang Technological University (NTU) students. The development of NTmU is an ongoing final year project led by Associate Professor Hui Sui Cheung, and was initially established by NTU students Chia Ching Chuen, Le Tan Khang and Ch...
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Nanyang Technological University
2021
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sg-ntu-dr.10356-1481662021-04-24T06:50:06Z Machine learning for new friends recommendation in NTU Niu, Jianan Hui Siu Cheung School of Computer Science and Engineering ASSCHUI@ntu.edu.sg Engineering::Computer science and engineering::Computing methodologies::Document and text processing NiceTomeetU (NTmU) is a friend recommendation platform exclusive to Nanyang Technological University (NTU) students. The development of NTmU is an ongoing final year project led by Associate Professor Hui Sui Cheung, and was initially established by NTU students Chia Ching Chuen, Le Tan Khang and Chang Jun Hao. NTmU provides a supplementary platform for NTU students to connect with more people within the school. It aims to encourage more friendships between students across different courses and nationalities, thus potentially broadening students’ social circles within the institution. The initial version of the NTmU system consists of a web-based application built using the Django framework and a mobile-based application built using the Flutter framework. NTmU makes use of machine learning techniques for generating word vectors based on the user’s profile descriptions and hobbies. Cosine similarity is used for calculating the similarities between the vectors and matching similar users for recommendations. This report covers the main modifications and enhancements made to the web-based (Django) version of the NTmU system. Bachelor of Engineering (Computer Science) 2021-04-24T06:50:06Z 2021-04-24T06:50:06Z 2021 Final Year Project (FYP) Niu, J. (2021). Machine learning for new friends recommendation in NTU. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/148166 https://hdl.handle.net/10356/148166 en SCSE20-0335 application/pdf Nanyang Technological University |
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Engineering::Computer science and engineering::Computing methodologies::Document and text processing Niu, Jianan Machine learning for new friends recommendation in NTU |
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NiceTomeetU (NTmU) is a friend recommendation platform exclusive to Nanyang Technological University (NTU) students. The development of NTmU is an ongoing final year project led by Associate Professor Hui Sui Cheung, and was initially established by NTU students Chia Ching Chuen, Le Tan Khang and Chang Jun Hao.
NTmU provides a supplementary platform for NTU students to connect with more people within the school. It aims to encourage more friendships between students across different courses and nationalities, thus potentially broadening students’ social circles within the institution.
The initial version of the NTmU system consists of a web-based application built using the Django framework and a mobile-based application built using the Flutter framework. NTmU makes use of machine learning techniques for generating word vectors based on the user’s profile descriptions and hobbies. Cosine similarity is used for calculating the similarities between the vectors and matching similar users for recommendations.
This report covers the main modifications and enhancements made to the web-based (Django) version of the NTmU system. |
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Hui Siu Cheung |
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Hui Siu Cheung Niu, Jianan |
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Final Year Project |
author |
Niu, Jianan |
author_sort |
Niu, Jianan |
title |
Machine learning for new friends recommendation in NTU |
title_short |
Machine learning for new friends recommendation in NTU |
title_full |
Machine learning for new friends recommendation in NTU |
title_fullStr |
Machine learning for new friends recommendation in NTU |
title_full_unstemmed |
Machine learning for new friends recommendation in NTU |
title_sort |
machine learning for new friends recommendation in ntu |
publisher |
Nanyang Technological University |
publishDate |
2021 |
url |
https://hdl.handle.net/10356/148166 |
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1698713674082942976 |