Machine learning for new friends recommendation in NTU
NiceToMeetU (NTmU) was developed as a friend recommendation system to help undergraduate students in Nanyang Technological University (NTU) to make new friends on campus. While the project is a continuous effort, my work involves mainly creating a software application for users. Previous work...
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Nanyang Technological University
2022
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sg-ntu-dr.10356-1566722022-04-22T05:15:53Z Machine learning for new friends recommendation in NTU Kang, Danson Kang Yit Hui Siu Cheung School of Computer Science and Engineering ASSCHUI@ntu.edu.sg Engineering::Computer science and engineering NiceToMeetU (NTmU) was developed as a friend recommendation system to help undergraduate students in Nanyang Technological University (NTU) to make new friends on campus. While the project is a continuous effort, my work involves mainly creating a software application for users. Previous work aimed to improve the matching algorithm using machine learning, which included: - Construction of user-profiles through questionnaires, which helped to collect textual data from users. - Deep learning approaches such as the Bidirectional Encoder Representations from Transformers (BERT). - Web application designed to collect more featuring data from our users including movies and music. My FYP focuses on delivering a user-friendly mobile application that encourages user usage of the NTmU platform. While there was initially a previous mobile application built, it still lacked certain functionality like the forum and chat. As such, the opportunity to leverage these functionalities as tools to collect textual data, as well as a more compelling reason to increase their screentime on NTmU, was missed out on. Bachelor of Engineering (Computer Science) 2022-04-22T05:15:53Z 2022-04-22T05:15:53Z 2022 Final Year Project (FYP) Kang, D. K. Y. (2022). Machine learning for new friends recommendation in NTU. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/156672 https://hdl.handle.net/10356/156672 en application/pdf Nanyang Technological University |
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Engineering::Computer science and engineering Kang, Danson Kang Yit Machine learning for new friends recommendation in NTU |
description |
NiceToMeetU (NTmU) was developed as a friend recommendation system to help
undergraduate students in Nanyang Technological University (NTU) to make new friends on
campus. While the project is a continuous effort, my work involves mainly creating a
software application for users.
Previous work aimed to improve the matching algorithm using machine learning, which
included:
- Construction of user-profiles through questionnaires, which helped to collect textual
data from users.
- Deep learning approaches such as the Bidirectional Encoder Representations from
Transformers (BERT).
- Web application designed to collect more featuring data from our users including
movies and music.
My FYP focuses on delivering a user-friendly mobile application that encourages user usage
of the NTmU platform. While there was initially a previous mobile application built, it still
lacked certain functionality like the forum and chat. As such, the opportunity to leverage
these functionalities as tools to collect textual data, as well as a more compelling reason to
increase their screentime on NTmU, was missed out on. |
author2 |
Hui Siu Cheung |
author_facet |
Hui Siu Cheung Kang, Danson Kang Yit |
format |
Final Year Project |
author |
Kang, Danson Kang Yit |
author_sort |
Kang, Danson Kang Yit |
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 |
2022 |
url |
https://hdl.handle.net/10356/156672 |
_version_ |
1731235772670935040 |