Citation

Ong, Kyle (2021) Hybrid deep-learning approach for document-context aware recommendation system. Masters thesis, Multimedia University.

Full text not available from this repository.

Abstract

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).

Item Type:	Thesis (Masters)
Additional Information:	Call No.: Q325.5 .O54 2021
Uncontrolled Keywords:	Machine learning
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics
Divisions:	Faculty of Computing and Informatics (FCI)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	20 Sep 2024 04:34
Last Modified:	20 Sep 2024 04:34
URII:	http://shdl.mmu.edu.my/id/eprint/12981

Downloads

Edit (login required)