Design of an Unsupervised Machine Learning-Based Movie Recommender System

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dc.contributor.authorPutri, Debby Cintia Ganeshacs
dc.contributor.authorLeu, Jenq-Shioucs
dc.contributor.authorĹ eda, Pavelcs
dc.coverage.issue2cs
dc.coverage.volume12cs
dc.date.accessioned2020-07-30T06:56:59Z
dc.date.available2020-07-30T06:56:59Z
dc.date.issued2020-01-21cs
dc.description.abstractThis 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.en
dc.formattextcs
dc.format.extent185-211cs
dc.format.mimetypeapplication/pdfcs
dc.identifier.citationSymmetry. 2020, vol. 12, issue 2, p. 185-211.en
dc.identifier.doi10.3390/sym12020185cs
dc.identifier.issn2073-8994cs
dc.identifier.other161377cs
dc.identifier.urihttp://hdl.handle.net/11012/193383
dc.language.isoencs
dc.publisherMDPIcs
dc.relation.ispartofSymmetrycs
dc.relation.urihttps://www.mdpi.com/2073-8994/12/2/185cs
dc.rightsCreative Commons Attribution 4.0 Internationalcs
dc.rights.accessopenAccesscs
dc.rights.sherpahttp://www.sherpa.ac.uk/romeo/issn/2073-8994/cs
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/cs
dc.subjectaffinity propagationen
dc.subjectagglomerative spectral clusteringen
dc.subjectassociation rule with Apriori algorithmen
dc.subjectaverage similarityen
dc.subjectbirchen
dc.subjectclustering performance evaluationen
dc.subjectcomputational timeen
dc.subjectDunn~Matrixen
dc.subjectmean-shiften
dc.subjectmean squared erroren
dc.subjectmini-batch K-Meansen
dc.subjectrecommendations systemen
dc.subjectK-Meansen
dc.subjectsocial network analysisen
dc.titleDesign of an Unsupervised Machine Learning-Based Movie Recommender Systemen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen
sync.item.dbidVAV-161377en
sync.item.dbtypeVAVen
sync.item.insts2021.02.25 16:53:12en
sync.item.modts2021.02.25 16:13:31en
thesis.grantorVysoké učení technické v Brně. Fakulta elektrotechniky a komunikačních technologií. Ústav telekomunikacícs
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