Course recommendation systems (back-end)

Recommender systems have been widely used in different domains such as e-commerce and streaming services to provide personalized recommendations to its users. The underlying concept to recommend item is mainly based on the user and item interactions and the learned embeddings of these interactions....

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Main Author: Lin, Myat Htet
Other Authors: Andy Khong W H
Format: Final Year Project
Language:English
Published: Nanyang Technological University 2024
Subjects:
Online Access:https://hdl.handle.net/10356/177138
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1771382024-05-31T15:44:48Z Course recommendation systems (back-end) Lin, Myat Htet Andy Khong W H School of Electrical and Electronic Engineering AndyKhong@ntu.edu.sg Computer and Information Science Engineering Recommender systems have been widely used in different domains such as e-commerce and streaming services to provide personalized recommendations to its users. The underlying concept to recommend item is mainly based on the user and item interactions and the learned embeddings of these interactions. It is important to consider other types of information available such as the different modalities of items such as visual, textual and acoustic. Basic recommender systems mainly focus on user-item interactions, but it is insufficient to solely focus on them as they are unable to capture hidden dependencies between users and item modalities. This Final Year Project (FYP) focuses on implementing the model Muli-Modal Self Supervised Learning Recommender System. The state-of-the-art model investigates the visual, textual and acoustic modalities of item and the effects that it has on the accuracy of the recommendations when integrating these modalities into user and item embeddings. The model also aims to alleviate problems in recommendation systems such as data scarcity through self-supervised learning techniques such as Generative Adversarial Networks. Four public datasets (TikTok, Amazon-Baby, Amazon-Sports, All-Recipes) are used to evaluate the performance of the model by using common evaluation metrics such as recall, precision and NDCG. Results obtained are compared to other baseline papers and shows how the model outperforms existing data. Lastly, the implemented model can be translated into other types of recommendations such as course recommendations for NTU’s students and teaching staff. It could provide personalized course recommendations to students to improve their academic performance. Bachelor's degree 2024-05-27T05:30:39Z 2024-05-27T05:30:39Z 2024 Final Year Project (FYP) Lin, M. H. (2024). Course recommendation systems (back-end). Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/177138 https://hdl.handle.net/10356/177138 en A3264-231 application/pdf Nanyang Technological University
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Computer and Information Science
Engineering
spellingShingle Computer and Information Science
Engineering
Lin, Myat Htet
Course recommendation systems (back-end)
description Recommender systems have been widely used in different domains such as e-commerce and streaming services to provide personalized recommendations to its users. The underlying concept to recommend item is mainly based on the user and item interactions and the learned embeddings of these interactions. It is important to consider other types of information available such as the different modalities of items such as visual, textual and acoustic. Basic recommender systems mainly focus on user-item interactions, but it is insufficient to solely focus on them as they are unable to capture hidden dependencies between users and item modalities. This Final Year Project (FYP) focuses on implementing the model Muli-Modal Self Supervised Learning Recommender System. The state-of-the-art model investigates the visual, textual and acoustic modalities of item and the effects that it has on the accuracy of the recommendations when integrating these modalities into user and item embeddings. The model also aims to alleviate problems in recommendation systems such as data scarcity through self-supervised learning techniques such as Generative Adversarial Networks. Four public datasets (TikTok, Amazon-Baby, Amazon-Sports, All-Recipes) are used to evaluate the performance of the model by using common evaluation metrics such as recall, precision and NDCG. Results obtained are compared to other baseline papers and shows how the model outperforms existing data. Lastly, the implemented model can be translated into other types of recommendations such as course recommendations for NTU’s students and teaching staff. It could provide personalized course recommendations to students to improve their academic performance.
author2 Andy Khong W H
author_facet Andy Khong W H
Lin, Myat Htet
format Final Year Project
author Lin, Myat Htet
author_sort Lin, Myat Htet
title Course recommendation systems (back-end)
title_short Course recommendation systems (back-end)
title_full Course recommendation systems (back-end)
title_fullStr Course recommendation systems (back-end)
title_full_unstemmed Course recommendation systems (back-end)
title_sort course recommendation systems (back-end)
publisher Nanyang Technological University
publishDate 2024
url https://hdl.handle.net/10356/177138
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