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Open AccessArticle

Design of an Unsupervised Machine Learning-Based Movie Recommender System

1
Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei City 106, Taiwan
2
Department of Telecommunications, Brno University of Technology, Technicka 12, 61600 Brno, Czech Republic
3
Institute of Computer Science, Masaryk University, Botanica 554/68A, 602 00 Brno, Czech Republic
*
Author to whom correspondence should be addressed.
Symmetry 2020, 12(2), 185; https://doi.org/10.3390/sym12020185
Received: 25 December 2019 / Revised: 11 January 2020 / Accepted: 13 January 2020 / Published: 21 January 2020
This research aims to determine the similarities in groups of people to build a film recommender system for users. Users often have difficulty in finding suitable movies due to the increasing amount of movie information. The recommender system is very useful for helping customers choose a preferred movie with the existing features. In this study, the recommender system development is established by using several algorithms to obtain groupings, such as the K-Means algorithm, birch algorithm, mini-batch K-Means algorithm, mean-shift algorithm, affinity propagation algorithm, agglomerative clustering algorithm, and spectral clustering algorithm. We propose methods optimizing K so that each cluster may not significantly increase variance. We are limited to using groupings based on Genre and Tags for movies. This research can discover better methods for evaluating clustering algorithms. To verify the quality of the recommender system, we adopted the mean square error (MSE), such as the Dunn Matrix and Cluster Validity Indices, and social network analysis (SNA), such as Degree Centrality, Closeness Centrality, and Betweenness Centrality. We also used average similarity, computational time, association rule with Apriori algorithm, and clustering performance evaluation as evaluation measures to compare method performance of recommender systems using Silhouette Coefficient, Calinski-Harabaz Index, and Davies–Bouldin Index.
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Keywords: affinity propagation; agglomerative spectral clustering; association rule with Apriori algorithm; average similarity; birch; clustering performance evaluation; computational time; Dunn Matrix; mean-shift; mean squared error; mini-batch K-Means; recommendations system; K-Means; social network analysis affinity propagation; agglomerative spectral clustering; association rule with Apriori algorithm; average similarity; birch; clustering performance evaluation; computational time; Dunn Matrix; mean-shift; mean squared error; mini-batch K-Means; recommendations system; K-Means; social network analysis
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MDPI and ACS Style

Cintia Ganesha Putri, D.; Leu, J.-S.; Seda, P. Design of an Unsupervised Machine Learning-Based Movie Recommender System. Symmetry 2020, 12, 185. https://doi.org/10.3390/sym12020185

AMA Style

Cintia Ganesha Putri D, Leu J-S, Seda P. Design of an Unsupervised Machine Learning-Based Movie Recommender System. Symmetry. 2020; 12(2):185. https://doi.org/10.3390/sym12020185

Chicago/Turabian Style

Cintia Ganesha Putri, Debby; Leu, Jenq-Shiou; Seda, Pavel. 2020. "Design of an Unsupervised Machine Learning-Based Movie Recommender System" Symmetry 12, no. 2: 185. https://doi.org/10.3390/sym12020185

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