{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T04:30:30Z","timestamp":1772253030837,"version":"3.50.1"},"reference-count":39,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2020,1,21]],"date-time":"2020-01-21T00:00:00Z","timestamp":1579564800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>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\u2013Bouldin Index.<\/jats:p>","DOI":"10.3390\/sym12020185","type":"journal-article","created":{"date-parts":[[2020,1,27]],"date-time":"2020-01-27T07:41:11Z","timestamp":1580110871000},"page":"185","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":25,"title":["Design of an Unsupervised Machine Learning-Based Movie Recommender System"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3059-2804","authenticated-orcid":false,"given":"Debby","family":"Cintia Ganesha Putri","sequence":"first","affiliation":[{"name":"Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei City 106, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7197-9912","authenticated-orcid":false,"given":"Jenq-Shiou","family":"Leu","sequence":"additional","affiliation":[{"name":"Department of Electronic and Computer Engineering, National Taiwan University of Science and Technology, Taipei City 106, Taiwan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6689-1980","authenticated-orcid":false,"given":"Pavel","family":"Seda","sequence":"additional","affiliation":[{"name":"Department of Telecommunications, Brno University of Technology, Technicka 12, 61600 Brno, Czech Republic"},{"name":"Institute of Computer Science, Masaryk University, Botanica 554\/68A, 602 00 Brno, Czech Republic"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1016\/j.eij.2015.06.005","article-title":"Recommendation systems: Principles, methods and evaluation","volume":"16","author":"Isinkaye","year":"2015","journal-title":"Egypt. Inform. J."},{"key":"ref_2","first-page":"44","article-title":"A new method for collaborative filtering recommender systems: The case of yahoo! movies and tripadvisor datasets","volume":"3","author":"Nilashi","year":"2016","journal-title":"J. Soft Comput. Decis. Support Syst."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1109\/MIC.2017.72","article-title":"Two decades of recommender systems at Amazon. com","volume":"21","author":"Smith","year":"2017","journal-title":"IEEE Int. Comput."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1016\/j.knosys.2018.02.026","article-title":"Personal price aware multi-seller recommender system: Evidence from eBay","volume":"150","author":"Rokach","year":"2018","journal-title":"Knowl. Syst."},{"key":"ref_5","first-page":"234","article-title":"Using recommendation systems in course management systems to recommend learning objects","volume":"5","author":"Itmazi","year":"2008","journal-title":"Int. Arab J. Inform. Technol."},{"key":"ref_6","first-page":"7","article-title":"A movie recommender system: Movrec","volume":"124","author":"Kumar","year":"2015","journal-title":"Int. J. Comput. Appl."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.dss.2015.03.008","article-title":"Recommender system application developments: A survey","volume":"74","author":"Lu","year":"2015","journal-title":"Decis. Support Syst."},{"key":"ref_8","first-page":"30","article-title":"Document clustering: A detailed review","volume":"4","author":"Shah","year":"2012","journal-title":"Int. J. Appl. Inform. Syst."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"114472","DOI":"10.1109\/ACCESS.2019.2934179","article-title":"A Feature-Reduction Multi-View k-Means Clustering Algorithm","volume":"7","author":"Yang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1016\/j.asoc.2016.01.020","article-title":"A patent quality analysis and classification system using self-organizing maps with support vector machine","volume":"41","author":"Wu","year":"2016","journal-title":"Appl. Soft Comput."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"78434","DOI":"10.1109\/ACCESS.2019.2922737","article-title":"Statistics-Enhanced Direct Batch Growth Self-Organizing Mapping for Efficient DoS Attack Detection","volume":"7","author":"Qu","year":"2019","journal-title":"IEEE Access"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"34425","DOI":"10.1109\/ACCESS.2019.2892648","article-title":"Novel land cover change detection method based on K-means clustering and adaptive majority voting using bitemporal remote sensing images","volume":"7","author":"Lv","year":"2019","journal-title":"IEEE Access"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"667","DOI":"10.1016\/j.jvlc.2014.09.011","article-title":"An improved collaborative movie recommendation system using computational intelligence","volume":"25","author":"Wang","year":"2014","journal-title":"J. Vis. Lang. Comput."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Himel, M.T., Uddin, M.N., Hossain, M.A., and Jang, Y.M. (2017, January 18\u201320). Weight based movie recommendation system using K-means algorithm. Proceedings of the 2017 International Conference on Information and Communication Technology Convergence (ICTC), Jeju-do, Korea.","DOI":"10.1109\/ICTC.2017.8190928"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"4431","DOI":"10.1109\/ACCESS.2016.2627527","article-title":"Hybrid clustering scheme for relaying in multi-cell LTE high user density networks","volume":"5","author":"Hajjar","year":"2017","journal-title":"IEEE Access"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"764","DOI":"10.1016\/j.procs.2015.06.090","article-title":"Image segmentation using K-means clustering algorithm and subtractive clustering algorithm","volume":"54","author":"Dhanachandra","year":"2015","journal-title":"Procedia Comput. Sci."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"507","DOI":"10.1016\/j.procs.2016.02.095","article-title":"Analysis of k-means and k-medoids algorithm for big data","volume":"78","author":"Arora","year":"2016","journal-title":"Procedia Comput. Sci."},{"key":"ref_18","unstructured":"Yang, Y., Wu, L., Guo, J., and Liu, S. (2012, January 15\u201317). Research on distributed Hilbert R tree spatial index based on BIRCH clustering. Proceedings of the 2012 20th International Conference on Geoinformatics, Hong Kong, China."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"11897","DOI":"10.1109\/ACCESS.2018.2810267","article-title":"Clustering approach based on mini batch kmeans for intrusion detection system over big data","volume":"6","author":"Peng","year":"2018","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Chen, Y., Hu, P., and Wang, W. (2018, January 13\u201315). Improved K-Means Algorithm and its Implementation Based on Mean Shift. Proceedings of the 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), Beijing, China.","DOI":"10.1109\/CISP-BMEI.2018.8633100"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"558","DOI":"10.1109\/LCOMM.2016.2517017","article-title":"Low-energy adaptive clustering hierarchy using affinity propagation for wireless sensor networks","volume":"20","author":"Sohn","year":"2016","journal-title":"IEEE Commun. Lett."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"562","DOI":"10.1080\/00207721.2013.797037","article-title":"Hesitant fuzzy agglomerative hierarchical clustering algorithms","volume":"46","author":"Zhang","year":"2015","journal-title":"Int. J. Syst. Sci."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"172","DOI":"10.1016\/j.patcog.2016.01.035","article-title":"Global discriminative-based nonnegative spectral clustering","volume":"55","author":"Shang","year":"2016","journal-title":"Pattern Recognit."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"19","DOI":"10.1145\/2827872","article-title":"The movielens datasets: History and context","volume":"5","author":"Harper","year":"2016","journal-title":"Acm Trans. Interact. Intell. Syst."},{"key":"ref_25","unstructured":"Robert, C. (1939). Cluster Analysis: Correlation Profile and Orthometric (Factor) Analysis for the Isolation of Unities in Mind and Personality, Edwards Brothers."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1089\/brain.2016.0475","article-title":"FMRI clustering in AFNI: False-positive rates redux","volume":"7","author":"Cox","year":"2017","journal-title":"Brain Connect."},{"key":"ref_27","first-page":"577","article-title":"Constrained k-means clustering with background knowledge","volume":"1","author":"Wagstaff","year":"2001","journal-title":"ICML"},{"key":"ref_28","first-page":"90","article-title":"Review on determining number of Cluster in K-Means Clustering","volume":"1","author":"Kodinariya","year":"2013","journal-title":"Int. J."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Liang, N., Zheng, H.T., Chen, J.Y., Sangaiah, A., and Zhao, C.Z. (2018). TRSDL: Tag-Aware Recommender System Based on Deep Learning\u2013Intelligent Computing Systems. Appl. Sci., 8.","DOI":"10.3390\/app8050799"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1016\/S0003-2670(00)82910-4","article-title":"Least median of squares: A robust method for outlier and model error detection in regression and calibration","volume":"187","author":"Massart","year":"1986","journal-title":"Anal. Chim. Acta"},{"key":"ref_31","first-page":"428","article-title":"Wavecluster: A multi-resolution clustering approach for very large spatial databases","volume":"98","author":"Sheikholeslami","year":"1998","journal-title":"VLDB"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Maimon, O., and Rokach, L. (2005). Data Mining and Knowledge Discovery Handbook, Springer.","DOI":"10.1007\/b107408"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1109\/TIT.1975.1055330","article-title":"The estimation of the gradient of a density function, with applications in pattern recognition","volume":"21","author":"Fukunaga","year":"1975","journal-title":"IEEE Trans. Inform. Theory"},{"key":"ref_34","unstructured":"Dueck, D. (2009). Affinity Propagation: Clustering Data by Passing Messages, University of Toronto."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"020052","DOI":"10.1063\/1.4994455","article-title":"Using k-means++ algorithm for researchers clustering","volume":"Volume 1867","author":"Rukmi","year":"2017","journal-title":"AIP Conference Proceedings"},{"key":"ref_36","unstructured":"Freeman, L. (2004). The Development of Social Network Analysis, Empirical Press. A Study in the Sociology of Science."},{"key":"ref_37","unstructured":"Plattel, C. (2014). Distributed and Incremental Clustering Using Shared Nearest Neighbours. [Master\u2019s Thesis, Utrecht University]."},{"key":"ref_38","unstructured":"Malik, J.S., Goyal, P., and Sharma, A.K. A Comprehensive Approach towards Data Preprocessing Techniques & Association Rules. In Proceedings of the 4th National Conference. Available online: http:\/\/bvicam.ac.in\/news\/INDIACom%202010%20Proceedings\/papers\/Group3\/INDIACom10_279_Paper%20(2).pdf."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1650","DOI":"10.1109\/TPAMI.2002.1114856","article-title":"Performance evaluation of some clustering algorithms and validity indices","volume":"24","author":"Maulik","year":"2002","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."}],"container-title":["Symmetry"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/2\/185\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,13]],"date-time":"2025-10-13T13:30:00Z","timestamp":1760362200000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2073-8994\/12\/2\/185"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,1,21]]},"references-count":39,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["sym12020185"],"URL":"https:\/\/doi.org\/10.3390\/sym12020185","relation":{"has-preprint":[{"id-type":"doi","id":"10.20944\/preprints202001.0124.v1","asserted-by":"object"}]},"ISSN":["2073-8994"],"issn-type":[{"value":"2073-8994","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,1,21]]}}}