Preprint Article Version 1 This version is not peer-reviewed

Design of an Unsupervised Machine Learning-Based Movie Recommender System

Version 1 : Received: 11 January 2020 / Approved: 12 January 2020 / Online: 12 January 2020 (15:23:56 CET)

A peer-reviewed article of this Preprint also exists.

Cintia Ganesha Putri, D.; Leu, J.-S.; Seda, P. Design of an Unsupervised Machine Learning-Based Movie Recommender System. Symmetry 2020, 12, 185. Cintia Ganesha Putri, D.; Leu, J.-S.; Seda, P. Design of an Unsupervised Machine Learning-Based Movie Recommender System. Symmetry 2020, 12, 185.

Journal reference: Symmetry 2020, 12, 185
DOI: 10.3390/sym12020185

Abstract

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.

Subject Areas

affinity propagation; agglomerative spectral clustering; social network analysis; recommendations system; clustering performance evaluation

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