Recommender system using deep learning and matrix factorization

The recommender system is part of machine learning that responsible to provide product recommendation to consumers in e-commerce. This system has been adopted by almost every e-commerce company in the world including Amazon, Alibaba, iTunes, Google, and Netflix. Collaborative filtering (CF) is the m...

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Main Author:	Hanafi
Format:	Thesis
Language:	English English
Published:	2021
Subjects:	H Social Sciences (General) HF Commerce
Online Access:	http://eprints.utem.edu.my/id/eprint/26098/1/Recommender%20system%20using%20deep%20learning%20and%20matrix%20factorization.pdf http://eprints.utem.edu.my/id/eprint/26098/2/Recommender%20system%20using%20deep%20learning%20and%20matrix%20factorization.pdf
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id	my-utem-ep.26098
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spelling	my-utem-ep.260982022-11-15T12:49:47Z Recommender system using deep learning and matrix factorization 2021 Hanafi H Social Sciences (General) HF Commerce The recommender system is part of machine learning that responsible to provide product recommendation to consumers in e-commerce. This system has been adopted by almost every e-commerce company in the world including Amazon, Alibaba, iTunes, Google, and Netflix. Collaborative filtering (CF) is the most popular recommender system method. This method calculated the user behaviour similarities in the form of ratings. However, in reality, the number of ratings is difficult to obtain (0.3% - 4%). In 2006 when Netflix competition was held, many experts consider to using matrix factorization (latent factor) model, and this model has proved to be better performance over previous work using statistical method. However, the matrix factorization performs degrade when implemented on extreme sparse data. Adding information in the form of product documents using Latent Dirichlet Allocation (LDA) is one way to solve this problem. However, the LDA method also faces obstacles to capture the documents contextual understanding which mostly of work by using LDA as categorical Bag of Word (BOW). A significant effort to capture the contextual meaning in Natural Language Processing (NLP) application is by considering the subtle words and the words order. Another problem is that most researchers involve only a part of the information to aid the performance of the matrix factorization, namely the user information or product information. Aiming to deal with the contextual problem of the document, this research proposes the use of the Long Short-Term Memory (LTSM) and word embedding (WE) based on Global Vector for Word Representation (GLOVE) method. According to the testing and evaluation report, the LTSM and GLOVE which has proven to be successful to capture the contextual meaning of the document in qualitative tests. Aiming to deal with the matrix factorization’s decrease in performance, this research proposes to integrate LSTM and Probabilistic Matrix Factorization (PMF). According to the experiment report, LSTM-PMF superior over existing best perform using Convolutional Neural Network (CNN) and PMF achieve 1.4% on average. Aiming to deal with hybridization between user and item information representation, this study implements Stack Denoising Auto Encoder (SDAE) and LSTM into PMF. The experiment report shows that this model outperforms over previous work achieve 0.92% on average. In the future, aiming to improve the performance in capturing understanding of product document, the use of bidirectional word vector representation and another variant of deep learning needs to be considered. 2021 Thesis http://eprints.utem.edu.my/id/eprint/26098/ http://eprints.utem.edu.my/id/eprint/26098/1/Recommender%20system%20using%20deep%20learning%20and%20matrix%20factorization.pdf text en public http://eprints.utem.edu.my/id/eprint/26098/2/Recommender%20system%20using%20deep%20learning%20and%20matrix%20factorization.pdf text en validuser https://plh.utem.edu.my/cgi-bin/koha/opac-detail.pl?biblionumber=121352 phd doctoral Universiti Teknikal Malaysia Melaka Faculty of Information and Communication Technology Herman, Nanna Suryana
institution	Universiti Teknikal Malaysia Melaka
collection	UTeM Repository
language	English English
advisor	Herman, Nanna Suryana
topic	H Social Sciences (General) HF Commerce
spellingShingle	H Social Sciences (General) HF Commerce Hanafi Recommender system using deep learning and matrix factorization
description	The recommender system is part of machine learning that responsible to provide product recommendation to consumers in e-commerce. This system has been adopted by almost every e-commerce company in the world including Amazon, Alibaba, iTunes, Google, and Netflix. Collaborative filtering (CF) is the most popular recommender system method. This method calculated the user behaviour similarities in the form of ratings. However, in reality, the number of ratings is difficult to obtain (0.3% - 4%). In 2006 when Netflix competition was held, many experts consider to using matrix factorization (latent factor) model, and this model has proved to be better performance over previous work using statistical method. However, the matrix factorization performs degrade when implemented on extreme sparse data. Adding information in the form of product documents using Latent Dirichlet Allocation (LDA) is one way to solve this problem. However, the LDA method also faces obstacles to capture the documents contextual understanding which mostly of work by using LDA as categorical Bag of Word (BOW). A significant effort to capture the contextual meaning in Natural Language Processing (NLP) application is by considering the subtle words and the words order. Another problem is that most researchers involve only a part of the information to aid the performance of the matrix factorization, namely the user information or product information. Aiming to deal with the contextual problem of the document, this research proposes the use of the Long Short-Term Memory (LTSM) and word embedding (WE) based on Global Vector for Word Representation (GLOVE) method. According to the testing and evaluation report, the LTSM and GLOVE which has proven to be successful to capture the contextual meaning of the document in qualitative tests. Aiming to deal with the matrix factorization’s decrease in performance, this research proposes to integrate LSTM and Probabilistic Matrix Factorization (PMF). According to the experiment report, LSTM-PMF superior over existing best perform using Convolutional Neural Network (CNN) and PMF achieve 1.4% on average. Aiming to deal with hybridization between user and item information representation, this study implements Stack Denoising Auto Encoder (SDAE) and LSTM into PMF. The experiment report shows that this model outperforms over previous work achieve 0.92% on average. In the future, aiming to improve the performance in capturing understanding of product document, the use of bidirectional word vector representation and another variant of deep learning needs to be considered.
format	Thesis
qualification_name	Doctor of Philosophy (PhD.)
qualification_level	Doctorate
author	Hanafi
author_facet	Hanafi
author_sort	Hanafi
title	Recommender system using deep learning and matrix factorization
title_short	Recommender system using deep learning and matrix factorization
title_full	Recommender system using deep learning and matrix factorization
title_fullStr	Recommender system using deep learning and matrix factorization
title_full_unstemmed	Recommender system using deep learning and matrix factorization
title_sort	recommender system using deep learning and matrix factorization
granting_institution	Universiti Teknikal Malaysia Melaka
granting_department	Faculty of Information and Communication Technology
publishDate	2021
url	http://eprints.utem.edu.my/id/eprint/26098/1/Recommender%20system%20using%20deep%20learning%20and%20matrix%20factorization.pdf http://eprints.utem.edu.my/id/eprint/26098/2/Recommender%20system%20using%20deep%20learning%20and%20matrix%20factorization.pdf
_version_	1776103137946370048

Recommender system using deep learning and matrix factorization

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