Publication detail

Design of an Unsupervised Machine Learning-Based Movie Recommender System

PUTRI, D. LEU, J. ŠEDA, P.

Original Title

Design of an Unsupervised Machine Learning-Based Movie Recommender System

Type

journal article in Web of Science

Language

English

Original 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.

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

Authors

PUTRI, D.; LEU, J.; ŠEDA, P.

Released

21. 1. 2020

Publisher

MDPI

ISBN

2073-8994

Periodical

Symmetry

Year of study

12

Number

2

State

Swiss Confederation

Pages from

185

Pages to

211

Pages count

27

URL

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

Full text in the Digital Library

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

BibTex

@article{BUT161377,
  author="Debby Cintia Ganesha {Putri} and Jenq-Shiou {Leu} and Pavel {Šeda}",
  title="Design of an Unsupervised Machine Learning-Based Movie Recommender System",
  journal="Symmetry",
  year="2020",
  volume="12",
  number="2",
  pages="185--211",
  doi="10.3390/sym12020185",
  issn="2073-8994",
  url="https://www.mdpi.com/2073-8994/12/2/185"
}