Deep Learning Solution For Sparsity Problem To Improve Recommendation Quality

mariana, tiwuk and lestari, sri (2024) Deep Learning Solution For Sparsity Problem To Improve Recommendation Quality. Masters thesis, Institut Informatika dan Bisnis Darmajaya.

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Abstract

Recommendation systems have become indispensable across various platforms due to their ability to enhance personalized services. Howev-er, these systems face a critical challenge known as sparsity. Sparsity occurs when there are numerous gaps in data, making user preferences unknown. This results in less relevant recommendations, reducing sys-tem effectiveness and diminishing user satisfaction. Moreover, it can lead to missed business opportunities. The purpose of this study is to address the sparsity problem using Deep Learning to enhance recom-mendation quality. The research stages include literature review (SLR), data collection from the Netflix Prize dataset obtained from kaggle.com, data preprocessing, Deep Learning implementation, testing, analysis, and conclusions. The stages of this study are conducted litera-ture study (SLR), data collection, data preprocessing, Deep Learning implementation, testing and analysis, and conclusions. The method of this study is carried out data preprocessing and imputaion using several existing methods by using the Netflix Prize dataset, data taken from kaggle.com. The result of this study shows that the Deep Learning method is able to solve the sparsity problem to improve the quality of recommendations, because the experimental results states that the Root Mean Squared Error (RMSE) value is the smallest compared to the Ma-trix-Factorization, SVD, KNN and other methods

Item Type: Thesis (Masters)
Subjects: Ilmu Komputer
eTheses
Divisions: Pasca Sarjana > Magister Teknik Informatika
Depositing User: Tiwuk Mariana jumar
Date Deposited: 24 Mar 2025 00:56
Last Modified: 24 Mar 2025 00:56
URI: http://repo.darmajaya.ac.id/id/eprint/19871

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