Hybrid deep-learning approach for document-context aware recommendation system

Traditional Collaborative Filtering (CF) methods such as Matrix Factorization (MF) only considers user preferences as a linear combination of user and item latent factors, leading to limited learning capabilities. To overcome this, many researchers have incorporated Deep Learning (DL) techniques int...

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Main Author:	Ong, Kyle
Format:	Thesis
Published:	2021
Subjects:	Q300-390 Cybernetics
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id	my-mmu-ep.12981
record_format	uketd_dc
spelling	my-mmu-ep.129812024-09-20T04:34:19Z Hybrid deep-learning approach for document-context aware recommendation system 2021-11 Ong, Kyle Q300-390 Cybernetics Traditional Collaborative Filtering (CF) methods such as Matrix Factorization (MF) only considers user preferences as a linear combination of user and item latent factors, leading to limited learning capabilities. To overcome this, many researchers have incorporated Deep Learning (DL) techniques into traditional CF. However, DLbased CF methods still suffer from data sparsity and cold-start problems. Several researchers have suggested incorporating side information into CF, which eventually converts the CF method to a Hybrid-based (HB) method. Besides, many HB methods also lack document based approaches, which have proven to improve recommendation accuracy by incorporating text-based document data. This thesis proposes a document context-aware Recommender System (RS) using a hybrid DL approach, namely Convolutional Neural Matrix Factorization ++ (CNMF++). CNMF++ is a hybrid DL model that jointly integrates three models including Generalized Matrix Factorization ++ (GMF++), Multilayer Perceptron ++ (MLP++) and Convolutional Neural Network (CNN). GMF++ and MLP++ first perform user-item metadata feature extraction by using Stacked Denoising Autoencoder (SDAE). 2021-11 Thesis https://shdl.mmu.edu.my/12981/ http://erep.mmu.edu.my/ masters Multimedia University Faculty of Computing and Informatics (FCI) EREP ID: 10286
institution	Multimedia University
collection	MMU Institutional Repository
topic	Q300-390 Cybernetics
spellingShingle	Q300-390 Cybernetics Ong, Kyle Hybrid deep-learning approach for document-context aware recommendation system
description	Traditional Collaborative Filtering (CF) methods such as Matrix Factorization (MF) only considers user preferences as a linear combination of user and item latent factors, leading to limited learning capabilities. To overcome this, many researchers have incorporated Deep Learning (DL) techniques into traditional CF. However, DLbased CF methods still suffer from data sparsity and cold-start problems. Several researchers have suggested incorporating side information into CF, which eventually converts the CF method to a Hybrid-based (HB) method. Besides, many HB methods also lack document based approaches, which have proven to improve recommendation accuracy by incorporating text-based document data. This thesis proposes a document context-aware Recommender System (RS) using a hybrid DL approach, namely Convolutional Neural Matrix Factorization ++ (CNMF++). CNMF++ is a hybrid DL model that jointly integrates three models including Generalized Matrix Factorization ++ (GMF++), Multilayer Perceptron ++ (MLP++) and Convolutional Neural Network (CNN). GMF++ and MLP++ first perform user-item metadata feature extraction by using Stacked Denoising Autoencoder (SDAE).
format	Thesis
qualification_level	Master's degree
author	Ong, Kyle
author_facet	Ong, Kyle
author_sort	Ong, Kyle
title	Hybrid deep-learning approach for document-context aware recommendation system
title_short	Hybrid deep-learning approach for document-context aware recommendation system
title_full	Hybrid deep-learning approach for document-context aware recommendation system
title_fullStr	Hybrid deep-learning approach for document-context aware recommendation system
title_full_unstemmed	Hybrid deep-learning approach for document-context aware recommendation system
title_sort	hybrid deep-learning approach for document-context aware recommendation system
granting_institution	Multimedia University
granting_department	Faculty of Computing and Informatics (FCI)
publishDate	2021
_version_	1811768019930578944

Hybrid deep-learning approach for document-context aware recommendation system

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