Design of an Unsupervised Machine Learning-Based Movie Recommender System

Design of an Unsupervised Machine Learning-Based Movie Recommender System

WoS Article

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.

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.

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

PUTRI, D.; LEU, J.; ŠEDA, P.

2021

21.01.2020

MDPI

2073-8994

Symmetry-Basel

12

2

Swiss Confederation

185

211

27

https://www.mdpi.com/2073-8994/12/2/185

http://hdl.handle.net/11012/193383

symmetry-12-00185-v3