Movie Recommender Systems: Concepts, Methods, Challenges, and Future Directions

. 2022 Jun 29 ; 22 (13) : . [epub] 20220629

Jazyk angličtina Země Švýcarsko Médium electronic

Typ dokumentu časopisecké články, systematický přehled

Perzistentní odkaz   https://www.medvik.cz/link/pmid35808398

Movie recommender systems are meant to give suggestions to the users based on the features they love the most. A highly performing movie recommendation will suggest movies that match the similarities with the highest degree of performance. This study conducts a systematic literature review on movie recommender systems. It highlights the filtering criteria in the recommender systems, algorithms implemented in movie recommender systems, the performance measurement criteria, the challenges in implementation, and recommendations for future research. Some of the most popular machine learning algorithms used in movie recommender systems such as K-means clustering, principal component analysis, and self-organizing maps with principal component analysis are discussed in detail. Special emphasis is given to research works performed using metaheuristic-based recommendation systems. The research aims to bring to light the advances made in developing the movie recommender systems, and what needs to be performed to reduce the current challenges in implementing the feasible solutions. The article will be helpful to researchers in the broad area of recommender systems as well as practicing data scientists involved in the implementation of such systems.

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Alyari F., Navimipour N.J. Recommender systems: A systematic review of the state of the art literature and suggestions for future research. Kybernetes. 2018;47:985. doi: 10.1108/K-06-2017-0196. DOI

Caro-Martinez M., Jimenez-Diaz G., Recio-Garcia J.A. A theoretical model of explanations in recommender systems; Proceedings of the ICCBR; Stockholm, Sweden. 9–12 July 2018.

Gupta S. A Literature Review on Recommendation Systems. Int. Res. J. Eng. Technol. 2020;7:3600–3605.

Abdulla G.M., Borar S. Size recommendation system for fashion e-commerce; Proceedings of the KDD Workshop on Machine Learning Meets Fashion; Halifax, NS, Canada. 14 August 2017.

Aggarwal C.C. Recommender Systems. Springer; Berlin/Heidelberg, Germany: 2016. An Introduction to Recommender Systems; pp. 1–28. DOI

Ghazanfar M.A., Prugel-Bennett A. A scalable, accurate hybrid recommender system; Proceedings of the 2010 Third International Conference on Knowledge Discovery and Data Mining; Washington, DC, USA. 9–10 January 2010.

Deldjoo Y., Elahi M., Cremonesi P., Garzotto F., Piazzolla P., Quadrana M. Content-Based Video Recommendation System Based on Stylistic Visual Features. J. Data Semant. 2016;5:99–113. doi: 10.1007/s13740-016-0060-9. DOI

Alamdari P.M., Navimipour N.J., Hosseinzadeh M., Safaei A.A., Darwesh A. A Systematic Study on the Recommender Systems in the E-Commerce. IEEE Access. 2020;8:115694–115716. doi: 10.1109/ACCESS.2020.3002803. DOI

Cami B.R., Hassanpour H., Mashayekhi H. A content-based movie recommender system based on temporal user preferences; Proceedings of the 2017 3rd Iranian Conference on Intelligent Systems and Signal Processing (ICSPIS); Shahrood, Iran. 20–21 December 2017.

Beniwal R., Debnath K., Jha D., Singh M. Data Analytics and Management. Springer; Berlin/Heidelberg, Germany: 2021. Hybrid Recommender System Using Artificial Bee Colony Based on Graph Database; pp. 687–699. DOI

Çano E., Morisio M. Hybrid recommender systems: A systematic literature review. Intell. Data Anal. 2017;21:1487–1524. doi: 10.3233/IDA-163209. DOI

Schafer J.B., Konstan J.A., Riedl J. E-commerce recommendation applications. Data Min. Knowl. Discov. 2001;5:115–153. doi: 10.1023/A:1009804230409. DOI

Shen J., Zhou T., Chen L. Collaborative filtering-based recommendation system for big data. Int. J. Comput. Sci. Eng. 2020;21:219–225. doi: 10.1504/IJCSE.2020.105727. DOI

Dakhel G.M., Mahdavi M. A new collaborative filtering algorithm using K-means clustering and neighbors’ voting; Proceedings of the 11th International Conference on Hybrid Intelligent Systems (HIS); Malacca, Malaysia. 5–8 December 2011; pp. 179–184. DOI

Katarya R., Verma O.P. An effective collaborative movie recommender system with cuckoo search. Egypt. Inform. J. 2017;18:105–112. doi: 10.1016/j.eij.2016.10.002. DOI

Kumar B., Sharma N. Approaches, Issues and Challenges in Recommender Systems: A Systematic Review. Indian J. Sci. Technol. 2016;9:1–12. doi: 10.17485/ijst/2015/v8i1/94892. DOI

Colomo-Palacios R., García-Peñalvo F.J., Stantchev V., Misra S. Towards a social and context-aware mobile recommendation system for tourism. Pervasive Mob. Comput. 2017;38:505–515. doi: 10.1016/j.pmcj.2016.03.001. DOI

Chou J.-S., Bui D.-K. Modeling heating and cooling loads by artificial intelligence for energy-efficient building design. Energy Build. 2014;82:437–446. doi: 10.1016/j.enbuild.2014.07.036. DOI

Casillo M., Conte D., Lombardi M., Santaniello D., Valentino C. Recommender System for Digital Storytelling: A Novel Approach to Enhance Cultural Heritage; Proceedings of the Pattern Recognition, ICPR International Workshops and Challenges, ICPR 2021; Virtual. 10–15 January 2021; pp. 304–317. DOI

Baltrunas L., Ricci F. Experimental evaluation of context-dependent collaborative filtering using item splitting. User Model. User Adapt. Interact. 2013;24:7–34. doi: 10.1007/s11257-012-9137-9. DOI

Baltrunas L., Kaminskas M., Ludwig B., Moling O., Ricci F., Aydin A., Lüke K.-H., Schwaiger R. Incarmusic: Context-aware music recommendations in a car; Proceedings of the International Conference on Electronic Commerce and Web Technologies; Vienna, Austria. 4–5 September 2011.

Baltrunas L., Ludwig B., Peer S., Ricci F. Context relevance assessment and exploitation in mobile recommender systems. Pers. Ubiquitous Comput. 2011;16:507–526. doi: 10.1007/s00779-011-0417-x. DOI

Casillo M., Gupta B.B., Lombardi M., Lorusso A., Santaniello D., Valentino C. Context Aware Recommender Systems: A Novel Approach Based on Matrix Factorization and Contextual Bias. Electronics. 2022;11:1003. doi: 10.3390/electronics11071003. DOI

Baltrunas L., Ludwig B., Peer S., Ricci F. Context-Aware Places of Interest Recommendations for Mobile Users; Proceedings of the International Conference of Design, User Experience, and Usability; Orlando, FL, USA. 9–14 July 2011; pp. 531–540. DOI

Casillo M., De Santo M., Lombardi M., Mosca R., Santaniello D., Valentino C. Recommender Systems and Digital Storytelling to Enhance Tourism Experience in Cultural Heritage Sites; Proceedings of the IEEE International Conference on Smart Computing (SMARTCOMP); Irvine, CA, USA. 23–27 August 2021; pp. 323–328. DOI

Casillo M., Conte D., Lombardi M., Santaniello D., Troiano A., Valentino C. A Content-Based Recommender System for Hidden Cultural Heritage Sites Enhancing; Proceedings of the Sixth International Congress on Information and Communication Technology; London, UK. 25–26 February 2021; pp. 97–109.

Park D.H., Kim H.K., Choi I.Y., Kim J.K. A literature review and classification of recommender systems research. Expert Syst. Appl. 2012;39:10059–10072. doi: 10.1016/j.eswa.2012.02.038. DOI

Arulmozhivarman M., Deepak G. OWLW: Ontology Focused User Centric Architecture for Web Service Recommendation Based on LSTM and Whale Optimization; Proceedings of the European, Asian, Middle Eastern, North African Conference on Management & Information Systems; Istanbul, Turkey. 19–20 March 2021; pp. 334–344. DOI

Wang Z., Yu X., Feng N., Wang Z. An improved collaborative movie recommendation system using computational intelligence. J. Vis. Lang. Comput. 2014;25:667–675. doi: 10.1016/j.jvlc.2014.09.011. DOI

Vilakone P., Park D.-S., Xinchang K., Hao F. An Efficient movie recommendation algorithm based on improved k-clique. Hum. Cent. Comput. Inf. Sci. 2018;8:38. doi: 10.1186/s13673-018-0161-6. DOI

Cho Y.S., Moon S.C., Noh S.C., Ryu K.H. Implementation of personalized recommendation system using k-means clustering of item category based on RFM; Proceedings of the 2012 IEEE International Conference on Management of Innovation & Technology (ICMIT); Bali, Indonesia. 11–13 June 2012; pp. 378–383. DOI

Georgiou O., Tsapatsoulis N. Improving the Scalability of Recommender Systems by Clustering Using Genetic Algorithms; Proceedings of the International Conference on Artificial Neural Networks; Sanya, China. 23–24 October 2010; pp. 442–449. DOI

Ge Y., Zhao S., Zhou H., Pei C., Sun F., Ou W., Zhang Y. Understanding echo chambers in e-commerce recommender systems; Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval; Xi’an, China. 25–30 July 2020.

Wang Q., Ma Y., Zhao K., Tian Y. A Comprehensive Survey of Loss Functions in Machine Learning. Ann. Data Sci. 2020;9:187–212. doi: 10.1007/s40745-020-00253-5. DOI

Farashah M.V., Etebarian A., Azmi R., Dastjerdi R.E. A hybrid recommender system based-on link prediction for movie baskets analysis. J. Big Data. 2021;8:32. doi: 10.1186/s40537-021-00422-0. DOI

Ortega F., Mayor J., López-Fernández D., Lara-Cabrera R. CF4J 2.0: Adapting Collaborative Filtering for Java to new challenges of collaborative filtering based recommender systems. Knowl. Based Syst. 2020;215:106629. doi: 10.1016/j.knosys.2020.106629. DOI

Al-Bakri N.F., Hashim S.H. Reducing Data Sparsity in Recommender Systems. Al-Nahrain J. Sci. 2018;21:138–147. doi: 10.22401/JNUS.21.2.20. DOI

Al-Bakri N.F., Hashim S.H. Collaborative Filtering Recommendation Model Based on k-means Clustering. Al-Nahrain J. Sci. 2019;22:74–79. doi: 10.22401/ANJS.22.1.10. DOI

Ahuja R., Solanki A., Nayyar A. Movie Recommender System Using K-Means Clustering AND K-Nearest Neighbor; Proceedings of the 9th International Conference on Cloud Computing, Data Science & Engineering (Confluence); Noida, India. 10–11 January 2019; pp. 263–268. DOI

Balabanović M., Shoham Y. Fab: Content-based, collaborative recommendation. Commun. ACM. 1997;40:66–72. doi: 10.1145/245108.245124. DOI

Belavagi M.C., Muniyal B. Performance Evaluation of Supervised Machine Learning Algorithms for Intrusion Detection. Procedia Comput. Sci. 2016;89:117–123. doi: 10.1016/j.procs.2016.06.016. DOI

Markos A.I., Vozalis M.G., Margaritis K.G. An Optimal Scaling Approach to Collaborative Filtering Using Categorical Principal Component Analysis and Neighborhood Formation; Proceedings of the IFIP International Conference on Artificial Intelligence Applications and Innovations; Hersonissos, Greece. 25–27 June 2010; pp. 22–29. DOI

Kumar A., Sharma A. Proceedings of the International Conference on Frontiers of Intelligent Computing: Theory and Applications (FICTA) Springer; Berlin/Heidelberg, Germany: 2013. Alleviating sparsity and scalability issues in collaborative filtering based recommender systems.

Park D.-H., Kim H.-K., Choi I.-Y., Kim J.K. A Literature Review and Classification of Recommender Systems on Academic Journals. J. Intell. Inf. Syst. 2011;17:139–152.

Quijano-Sanchez L., Recio-Garcia J.A., Diaz-Agudo B., Jimenez-Diaz G. Social factors in group recommender systems. ACM Trans. Intell. Syst. Technol. 2013;4:1–30. doi: 10.1145/2414425.2414433. DOI

Himel M.T., Uddin M.N., Hossain M.A., Jang Y.M. Weight based movie recommendation system using K-means algorithm; Proceedings of the 2017 International Conference on Information and Communication Technology Convergence (ICTC); Jeju Island, Korea. 18–20 October 2017.

Kourtit K., Nijkamp P., Arribas D. Smart cities in perspective–a comparative European study by means of self-organizing maps. Innov. Eur. J. Soc. Sci. Res. 2012;25:229–246. doi: 10.1080/13511610.2012.660330. DOI

Singh A., Thakur N., Sharma A. A review of supervised machine learning algorithms; Proceedings of the 3rd International Conference on Computing for Sustainable Global Development (INDIACom); New Delhi, India. 16–18 March 2016.

Sovilj D., Raiko T., Oja E. Extending Self-Organizing Maps with uncertainty information of probabilistic PCA; Proceedings of the International Joint Conference on Neural Networks (IJCNN); Barcelona, Spain. 18–23 July 2010; pp. 1–7. DOI

Madadipouya K., Chelliah S. A literature review on recommender systems algorithms, techniques and evaluations. Broad Res. Artif. Intell. Neurosci. 2017;8:109–124.

Berus L., Klancnik S., Brezocnik M., Ficko M. Classifying Parkinson’s Disease Based on Acoustic Measures Using Artificial Neural Networks. Sensors. 2018;19:16. doi: 10.3390/s19010016. PubMed DOI PMC

Shi Y., Larson M., Hanjalic A. Collaborative filtering beyond the user-item matrix: A survey of the state of the art and future challenges. ACM Comput. Surv. 2014;47:1–45. doi: 10.1145/2556270. DOI

Karaboga D. Artificial bee colony algorithm. Scholarpedia. 2010;5:6915. doi: 10.4249/scholarpedia.6915. DOI

Zhang Z.-K., Liu C., Zhang Y.-C., Zhou T. Solving the cold-start problem in recommender systems with social tags. Eur. Lett. 2010;92:28002. doi: 10.1209/0295-5075/92/28002. DOI

Kumar M.S., Prabhu J. Hybrid model for movie recommendation system using fireflies and fuzzy c-means. Int. J. Web Portals. 2019;11:1–13. doi: 10.4018/IJWP.2019070101. DOI

Shanmugasundar G., Fegade V., Mahdal M., Kalita K. Optimization of Variable Stiffness Joint in Robot Manipulator Using a Novel NSWOA-MARCOS Approach. Processes. 2022;10:1074. doi: 10.3390/pr10061074. DOI

Kalita K., Ghadai R.K. Optimization of Plasma Enhanced Chemical Vapor Deposition Process Parameters for Hardness improvement of Diamond Like Carbon Coatings. Sci. Iran. 2022 doi: 10.24200/SCI.2022.56869.4952. DOI

Kalita K., Ghadai R.K., Chakraborty S. Parametric optimization of CVD process for DLC Thin film coatings: A comparative analysis. Sādhanā. 2022;47:57. doi: 10.1007/s12046-022-01842-1. DOI

Kalita K., Ghadai R.K., Bansod A. Sensitivity Analysis of GFRP Composite Drilling Parameters and Genetic Algorithm-Based Optimisation. Int. J. Appl. Metaheuristic Comput. 2022;13:1–17. doi: 10.4018/IJAMC.290539. DOI

Shankar R., Ganesh N., Čep R., Narayanan R.C., Pal S., Kalita K. Hybridized Particle Swarm—Gravitational Search Algorithm for Process Optimization. Processes. 2022;10:616. doi: 10.3390/pr10030616. DOI

Rajendran S., Ganesh N., Čep R., Narayanan R.C., Pal S., Kalita K. A Conceptual Comparison of Six Nature-Inspired Metaheuristic Algorithms in Process Optimization. Processes. 2022;10:197. doi: 10.3390/pr10020197. DOI

Kalita K., Pal S., Haldar S., Chakraborty S. A Hybrid TOPSIS-PR-GWO Approach for Multi-objective Process Parameter Optimization. Process Integr. Optim. Sustain. 2022:1–16. doi: 10.1007/s41660-022-00256-0. DOI

Joshi M., Ghadai R.K., Madhu S., Kalita K., Gao X.-Z. Comparison of NSGA-II, MOALO and MODA for Multi-Objective Optimization of Micro-Machining Processes. Materials. 2021;14:5109. doi: 10.3390/ma14175109. PubMed DOI PMC

Pal S., Kalita K., Haldar S. Genetic Algorithm-Based Fundamental Frequency Optimization of Laminated Composite Shells Carrying Distributed Mass. J. Inst. Eng. Ser. C. 2022;103:389–401. doi: 10.1007/s40032-021-00801-9. DOI

Kalita K., Dey P., Joshi M., Haldar S. A response surface modelling approach for multi-objective optimization of composite plates. Steel Compos. Struct. 2019;32:455–466.

Abdel-Basset M., Mohamed R., Elkomy O.M., Abouhawwash M. Recent metaheuristic algorithms with genetic operators for high-dimensional knapsack instances: A comparative study. Comput. Ind. Eng. 2022;166:107974. doi: 10.1016/j.cie.2022.107974. DOI

Joshi M., Kalita K., Jangir P., Ahmadianfar I., Chakraborty S. A Conceptual Comparison of Dragonfly Algorithm Variants for CEC-2021 Global Optimization Problems. Arab. J. Sci. Eng. 2022:1–31. doi: 10.1007/s13369-022-06880-9. DOI

Bacanin N., Zivkovic M., Bezdan T., Venkatachalam K., Abouhawwash M. Modified firefly algorithm for workflow scheduling in cloud-edge environment. Neural Comput. Appl. 2022;34:9043–9068. doi: 10.1007/s00521-022-06925-y. PubMed DOI PMC

Abdel-Basset M., Mohamed R., Abouhawwash M. A new fusion of whale optimizer algorithm with Kapur’s entropy for multi-threshold image segmentation: Analysis and validations. Artif. Intell. Rev. 2022:1–71. doi: 10.1007/s10462-022-10157-w. PubMed DOI PMC

Abdel-Basset M., Mohamed R., Abouhawwash M. Hybrid marine predators algorithm for image segmentation: Analysis and validations. Artif. Intell. Rev. 2021;55:3315–3367. doi: 10.1007/s10462-021-10086-0. PubMed DOI PMC

Abdel-Basset M., Mohamed R., AbdelAziz N.M., Abouhawwash M. HWOA: A hybrid whale optimization algorithm with a novel local minima avoidance method for multi-level thresholding color image segmentation. Expert Syst. Appl. 2021;190:116145. doi: 10.1016/j.eswa.2021.116145. DOI

Kant V., Bharadwaj K.K. Proceedings of the Seventh International Conference on Bio-Inspired Computing: Theories and Applications (BIC-TA 2012) Springer; New Delhi, India: 2013. A user-oriented content based recommender system based on reclusive methods and interactive genetic algorithm.

Koosha H.R., Ghorbani Z., Nikfetrat R. A Clustering-Classification Recommender System based on Firefly Algorithm. J. AI Data Min. 2022;10:103–116.

Karaboga D., Akay B. A comparative study of artificial bee colony algorithm. Appl. Math. Comput. 2009;214:108–132. doi: 10.1016/j.amc.2009.03.090. DOI

Chen M., Liu P. Performance evaluation of recommender systems. Int. J. Perform. Eng. 2017;13:1246. doi: 10.23940/ijpe.17.08.p7.12461256. DOI

Guy I., Zwerdling N., Ronen I., Carmel D., Uziel E. Social media recommendation based on people and tags; Proceedings of the 33rd International ACM SIGIR Conference on Research and Development in Information Retrieval; Geneva, Switzerland. 19–23 July 2010.

Zhu G., Kwong S. Gbest-guided artificial bee colony algorithm for numerical function optimization. Appl. Math. Comput. 2010;217:3166–3173. doi: 10.1016/j.amc.2010.08.049. DOI

Katarya R. Movie recommender system with metaheuristic artificial bee. Neural Comput. Appl. 2018;30:1983–1990. doi: 10.1007/s00521-017-3338-4. DOI

Bolaji A.L., Khader A.T., Al-Betar M.A., Awadallah M.A. Artificial bee colony algorithm, its variants and applications: A survey. J. Theor. Appl. Inf. Technol. 2013;47:434–459.

Haghgu Z., Hasheminejad S.M.H., Azmi R. A Novel Data Filtering for a Modified Cuckoo Search Based Movie Recommender; Proceedings of the 7th International Conference on Web Research (ICWR); Tehran, Iran. 19–20 May 2021; pp. 243–247. DOI

Katarya R., Verma O.P. Recommender system with grey wolf optimizer and FCM. Neural Comput. Appl. 2016;30:1679–1687. doi: 10.1007/s00521-016-2817-3. DOI

Sivaramakrishnan N., Subramaniyaswamy V., Ravi L., Vijayakumar V., Gao X.-Z., Sri S.R. An effective user clustering-based collaborative filtering recommender system with grey wolf optimization. Int. J. Bio Inspired Comput. 2020;16:44–55. doi: 10.1504/IJBIC.2020.108999. DOI

Papneja S., Sharma K., Khilwani N. Movie Recommendation to Friends Using Whale Optimization Algorithm, Recent Advances in Computer Science and Communications. Recent Pat. Comput. Sci. 2021;14:1470–1475. doi: 10.2174/2213275912666190823104600. DOI

Tripathi A.K., Mittal H., Saxena P., Gupta S. A new recommendation system using map-reduce-based tournament empowered Whale optimization algorithm. Complex Intell. Syst. 2020;7:297–309. doi: 10.1007/s40747-020-00200-0. DOI

Zhang Y., Wang S., Ji G. A Comprehensive Survey on Particle Swarm Optimization Algorithm and Its Applications. Math. Probl. Eng. 2015;2015:931256. doi: 10.1155/2015/931256. DOI

Singh P.K., Pramanik P.D., Choudhury P. New Age Analytics: Transforming the Internet through Machine Learning, IoT, and Trust Modeling. Apple Academic Press; Burlington, ON, Canada: 2020. Collaborative filtering in recommender systems: Technicalities, challenges, applications, and research trends; pp. 183–215.

Karaboga D., Ozturk C. A novel clustering approach: Artificial Bee Colony (ABC) algorithm. Appl. Soft Comput. 2011;11:652–657. doi: 10.1016/j.asoc.2009.12.025. DOI

Navin J.R.M., Pankaja R. Performance analysis of text classification algorithms using confusion matrix. Int. J. Eng. Tech. Res. 2016;6:75–78.

Marom N.D., Rokach L., Shmilovici A. Using the confusion matrix for improving ensemble classifiers; Proceedings of the IEEE 26th Convention of Electrical and Electronics Engineers; Eilat, Israel. 17–20 November 2010.

Mahmoud D.S., John R.I. Enhanced content-based filtering algorithm using Artificial Bee Colony optimisation; Proceedings of the SAI Intelligent Systems Conference (IntelliSys); London, UK. 10–11 November 2015; pp. 155–163. DOI

Nair A.M., Preethi N. IoT and Analytics for Sensor Networks. Springer; Berlin/Heidelberg, Germany: 2021. A Pragmatic Study on Movie Recommender Systems Using Hybrid Collaborative Filtering; pp. 489–494. DOI

Mathieu M., Couprie C., LeCun Y. Deep multi-scale video prediction beyond mean square error. arXiv. 20151511.05440

Yadav N., Mundotiya R.K., Singh A.K., Pal S. Diversity in Recommendation System: A Cluster Based Approach; Proceedings of the International Conference on Hybrid Intelligent Systems; Online. 14–16 December 2020; pp. 113–122. DOI

Ramzan B., Bajwa I.S., Jamil N., Amin R.U., Ramzan S., Mirza F., Sarwar N. An Intelligent Data Analysis for Recommendation Systems Using Machine Learning. Sci. Program. 2019;2019:5941096. doi: 10.1155/2019/5941096. DOI

Dhankhad S., Mohammed E., Far B. Supervised Machine Learning Algorithms for Credit Card Fraudulent Transaction Detection: A Comparative Study; Proceedings of the 2018 IEEE International Conference on Information Reuse and Integration (IRI); Salt Lake City, UT, USA. 6–10 July 2018; pp. 122–125. DOI

Santra A.K., Christy C.J. Genetic algorithm and confusion matrix for document clustering. Int. J. Comput. Sci. Issues. 2012;9:322.

Lam X.N., Vu T., Le T.D., Duong A.D. Addressing cold-start problem in recommendation systems; Proceedings of the 2nd International Conference on Ubiquitous Information Management and Communication; Suwon, Korea. 31 January–1 February 2008.

Liang T., Wu S., Cao D. Emerging Computation and Information teChnologies for Education. Springer; Berlin/Heidelberg, Germany: 2012. Improved Collaborative Filtering Method Applied in Movie Recommender System; pp. 427–432. DOI

Mekouar L., Iraqi Y., Damaj I., Naous T. A survey on blockchain-based Recommender Systems: Integration architecture and taxonomy. Comput. Commun. 2022;187:1–19. doi: 10.1016/j.comcom.2022.01.020. DOI

Schedl M., Zamani H., Chen C.-W., Deldjoo Y., Elahi M. Current challenges and visions in music recommender systems research. Int. J. Multimedia Inf. Retr. 2018;7:95–116. doi: 10.1007/s13735-018-0154-2. DOI

Das D., Chidananda H.T., Sahoo L. Progress in Computing, Analytics and Networking. Springer; Berlin/Heidelberg, Germany: 2018. Personalized Movie Recommendation System Using Twitter Data; pp. 339–347. DOI

Sarwar B.M. Sparsity, Scalability, and Distribution in Recommender Systems. University of Minnesota; Minneapolis, MN, USA: 2001.

Ponnam L.T., Punyasamudram S.D., Nallagulla S.N., Yellamati S. Movie recommender system using item based collaborative filtering technique; Proceedings of the International Conference on Emerging Trends in Engineering, Technology and Science (ICETETS); Pudukkottai, India. 24–26 February 2016.

Sethi D., Singhal A. Comparative analysis of a recommender system based on ant colony optimization and artificial bee colony optimization algorithms; Proceedings of the 8th International Conference on Computing, Communication and Networking Technologies (ICCCNT); Delhi, India. 3–5 July 2017; pp. 1–4. DOI

Zhou T., Chen L., Shen J. Movie recommendation system employing the user-based cf in cloud computing; Proceedings of the 2017 IEEE International Conference on Computational Science and Engineering (CSE) and IEEE International Conference on Embedded and Ubiquitous Computing (EUC); Guangzhou, China. 21–24 July 2017.

Shukla S., Naganna S. A review on K-means data clustering approach. Int. J. Inf. Comput. Technol. 2014;4:1847–1860.

Himeur Y., Sayed A., Alsalemi A., Bensaali F., Amira A., Varlamis I., Eirinaki M., Sardianos C., Dimitrakopoulos G. Blockchain-based recommender systems: Applications, challenges and future opportunities. Comput. Sci. Rev. 2022;43:100439. doi: 10.1016/j.cosrev.2021.100439. DOI

Singh M. Scalability and sparsity issues in recommender datasets: A survey. Knowl. Inf. Syst. 2018;62:1–43. doi: 10.1007/s10115-018-1254-2. DOI

Vellaichamy V., Kalimuthu V. Hybrid Collaborative Movie Recommender System Using Clustering and Bat Optimization. Int. J. Intell. Eng. Syst. 2017;10:38–47. doi: 10.22266/ijies2017.1031.05. DOI

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