Design of an Unsupervised Machine Learning-Based Movie Recommender System
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F20%3APU134917" target="_blank" >RIV/00216305:26220/20:PU134917 - isvavai.cz</a>
Result on the web
<a href="https://www.mdpi.com/2073-8994/12/2/185" target="_blank" >https://www.mdpi.com/2073-8994/12/2/185</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.3390/sym12020185" target="_blank" >10.3390/sym12020185</a>
Alternative languages
Result language
angličtina
Original language name
Design of an Unsupervised Machine Learning-Based Movie Recommender System
Original language description
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.
Czech name
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Czech description
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Classification
Type
J<sub>imp</sub> - Article in a specialist periodical, which is included in the Web of Science database
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
S - Specificky vyzkum na vysokych skolach
Others
Publication year
2020
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
Symmetry
ISSN
2073-8994
e-ISSN
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Volume of the periodical
12
Issue of the periodical within the volume
2
Country of publishing house
CH - SWITZERLAND
Number of pages
27
Pages from-to
185-211
UT code for WoS article
000521147600054
EID of the result in the Scopus database
2-s2.0-85080919258