Movie Magic: Developing a Personalized Recommendation System for Movie Enthusiasts using Machine Learning
Main Article Content
Abstract
One of the most popular and effective uses of machine learning in business is in recommender systems. This method of information filtering is employed to forecast the user's choice. The most frequently used areas for recommender systems are books, news, articles, music, videos, and movies, among other things. In this essay, we present a collaborative filtering-based movie recommendation system that uses user-provided data to analyse it and then suggests the movies that are most appropriate for the person at hand. The recommended movie list is arranged using a variety of machine learning techniques in accordance with the ratings that previous users have given these films.
Recommender systems are one of the most well-liked and successful applications of machine learning in business. The user's choice is predicted using this information filtering technique. The most often used categories for recommender systems include, among other things, books, news, articles, music, videos, and movies. In this paper, we offer a collaborative filtering-based system for movie selection that analyses user-provided data and then recommends the movies that are best suitable for the user at hand. Using a number of machine learning approaches, the recommended movie list is organised according to the ratings that previous users have given these movies.
Downloads
Article Details
How to Cite
References
K. U. Maheshwari and G. Shobana, "The State of the art tools and techniques for remote digital forensic investigations," 2021 3rd International Conference on Signal Processing and Communication (ICPSC), 2021, pp. 464-468, doi: 10.1109/ICSPC51351.2021.9451718.
L. Chen, L. Xu, X. Yuan and N. Shashidhar, "Digital forensics in social networks and the cloud: Process, approaches, methods, tools, and challenges," 2015 International Conference on Computing, Networking and Communications (ICNC), 2015, pp. 1132-1136, doi: 10.1109/ICCNC.2015.7069509.
K. S. Singh, A. Irfan and N. Dayal, "Cyber Forensics and Comparative Analysis of Digital Forensic Investigation Frameworks," 2019 4th International Conference on Information Systems and Computer Networks (ISCON), 2019, pp. 584-590, doi: 10.1109/ISCON47742.2019.9036214.
K. Ghazinour, D. M. Vakharia, K. C. Kannaji and R. Satyakumar, "A study on digital forensic tools," 2017 IEEE International Conference on Power, Control, Signals and Instrumentation Engineering (ICPCSI), 2017, pp. 3136-3142, doi: 10.1109/ICPCSI.2017.8392304.
A. Al-Sabaawi, "Digital Forensics for Infected Computer Disk and Memory: Acquire, Analyse, and Report," 2020 IEEE Asia-Pacific Conference on Computer Science and Data Engineering (CSDE), 2020, pp. 1-7, doi: 10.1109/CSDE50874.2020.9411614.
Chaitanya Krishna Suryadevara, “TOWARDS PERSONALIZED HEALTHCARE - AN INTELLIGENT MEDICATION RECOMMENDATION SYSTEM”, IEJRD - International Multidisciplinary Journal, vol. 5, no. 9, p. 16, Dec. 2020.
Kunduru, A. R. (2023). DATA CONVERSION STRATEGIES FOR ERP IMPLEMENTATION PROJECTS. CENTRAL ASIAN JOURNAL OF MATHEMATICAL THEORY AND COMPUTER SCIENCES, 4(9), 1-6. Retrieved from https://cajmtcs.centralasianstudies.org/index.php/CAJMTCS/article/view/509
Arjun Reddy Kunduru. (2023). Healthcare ERP Project Success: It’s all About Avoiding Missteps. Central Asian Journal of Theoretical and Applied Science, 4(8), 130-134. Retrieved from https://cajotas.centralasianstudies.org/index.php/CAJOTAS/article/view/1268
Kunduru, A. R. (2023). THE PERILS AND DEFENSES OF ENTERPRISE CLOUDCOMPUTING: A COMPREHENSIVE REVIEW. Central Asian Journal of Mathematical Theory and Computer Sciences, 4(9), 29-41.
Kunduru, A. R. (2023). Maximizing Business Value with Integrated IoT and Cloud ERP Systems. International Journal of Innovative Analyses and Emerging Technology, 3(9), 1-8.
Kunduru, A. R. (2023). Blockchain Technology for ERP Systems: A Review. American Journal of Engineering, Mechanics and Architecture, 1(7), 56-63.
Suryadevara, Chaitanya Krishna, Predictive Modeling for Student Performance: Harnessing Machine Learning to Forecast Academic Marks (December 22, 2018). International Journal of Research in Engineering and Applied Sciences (IJREAS), Vol. 8 Issue 12, December-2018, Available at SSRN: https://ssrn.com/abstract=4591990
Suryadevara, Chaitanya Krishna, Unveiling Urban Mobility Patterns: A Comprehensive Analysis of Uber (December 21, 2019). International Journal of Engineering, Science and Mathematics, Vol. 8 Issue 12, December 2019, Available at SSRN: https://ssrn.com/abstract=4591998
Chaitanya Krishna Suryadevara. (2019). A NEW WAY OF PREDICTING THE LOAN APPROVAL PROCESS USING ML TECHNIQUES. International Journal of Innovations in Engineering Research and Technology, 6(12), 38–48. Retrieved from https://repo.ijiert.org/index.php/ijiert/article/view/3654
Chaitanya Krishna Suryadevara. (2020). GENERATING FREE IMAGES WITH OPENAI’S GENERATIVE MODELS. International Journal of Innovations in Engineering Research and Technology, 7(3), 49–56. Retrieved from https://repo.ijiert.org/index.php/ijiert/article/view/3653
Chaitanya Krishna Suryadevara. (2020). REAL-TIME FACE MASK DETECTION WITH COMPUTER VISION AND DEEP LEARNING: English. International Journal of Innovations in Engineering Research and Technology, 7(12), 254–259. Retrieved from https://repo.ijiert.org/index.php/ijiert/article/view/3184
Chaitanya Krishna Suryadevara. (2021). ENHANCING SAFETY: FACE MASK DETECTION USING COMPUTER VISION AND DEEP LEARNING. International Journal of Innovations in Engineering Research and Technology, 8(08), 224–229. Retrieved from https://repo.ijiert.org/index.php/ijiert/article/view/3672
Kunduru, A. R. (2023). Security concerns and solutions for enterprise cloud computing applications. Asian Journal of Research in Computer Science, 15(4), 24–33. https://doi.org/10.9734/ajrcos/2023/v15i4327
Kunduru, A. R. (2023). Industry best practices on implementing oracle cloud ERP security. International Journal of Computer Trends and Technology, 71(6), 1-8. https://doi.org/10.14445/22312803/IJCTT-V71I6P101
Kunduru, A. R. (2023). Cloud Appian BPM (Business Process Management) Usage In health care Industry. IJARCCE International Journal of Advanced Research in Computer and Communication Engineering, 12(6), 339-343. https://doi.org/10.17148/IJARCCE.2023.12658
Kunduru, A. R. (2023). Effective usage of artificial intelligence in enterprise resource planning applications. International Journal of Computer Trends and Technology, 71(4), 73-80. https://doi.org/10.14445/22312803/IJCTT-V71I4P109
Kunduru, A. R. (2023). Recommendations to advance the cloud data analytics and chatbots by using machine learning technology. International Journal of Engineering and Scientific Research, 11(3), 8-20.
Kunduru, A. R., & Kandepu, R. (2023). Data archival methodology in enterprise resource planning applications (Oracle ERP, Peoplesoft). Journal of Advances in Mathematics and Computer Science, 38(9), 115–127. https://doi.org/10.9734/jamcs/2023/v38i91809
Chaitanya Krishna Suryadevara, “DIABETES RISK ASSESSMENT USING MACHINE LEARNING: A COMPARATIVE STUDY OF CLASSIFICATION ALGORITHMS”, IEJRD - International Multidisciplinary Journal, vol. 8, no. 4, p. 10, Aug. 2023.
Chaitanya Krishna Suryadevara. (2023). REVOLUTIONIZING DIETARY MONITORING: A COMPREHENSIVE ANALYSIS OF THE INNOVATIVE MOBILE APP FOR TRACKING DIETARY COMPOSITION. International Journal of Innovations in Engineering Research and Technology, 10(8), 44–50. Retrieved from https://repo.ijiert.org/index.php/ijiert/article/view/3673
Chaitanya krishna Suryadevara. (2023). NOVEL DEVICE TO DETECT FOOD CALORIES USING MACHINE LEARNING. Open Access Repository, 10(9), 52–61. Retrieved from https://oarepo.org/index.php/oa/article/view/3546
Kunduru, A. R. (2023). Artificial intelligence usage in cloud application performance improvement. Central Asian Journal of Mathematical Theory and Computer Sciences, 4(8), 42-47. https://cajmtcs.centralasianstudies.org/index.php/CAJMTCS/article/view/491
Kunduru, A. R. (2023). Artificial intelligence advantages in cloud Fintech application security. Central Asian Journal of Mathematical Theory and Computer Sciences, 4(8), 48-53. https://cajmtcs.centralasianstudies.org/index.php/CAJMTCS/article/view/492
Kunduru, A. R. (2023). Cloud BPM Application (Appian) Robotic Process Automation Capabilities. Asian Journal of Research in Computer Science, 16(3), 267–280. https://doi.org/10.9734/ajrcos/2023/v16i3361
Kunduru, A. R. (2023). Machine Learning in Drug Discovery: A Comprehensive Analysis of Applications, Challenges, and Future Directions. International Journal on Orange Technologies, 5(8), 29-37.
Arjun Reddy Kunduru. (2023). From Data Entry to Intelligence: Artificial Intelligence’s Impact on Financial System Workflows. International Journal on Orange Technologies, 5(8), 38-45. Retrieved from https://journals.researchparks.org/index.php/IJOT/article/view/4727
Arjun Reddy Kunduru. (2023). The Inevitability of Cloud-Based Case Management for Regulated Enterprises. International Journal of Discoveries and Innovations in Applied Sciences, 3(8), 13–18. Retrieved from https://openaccessjournals.eu/index.php/ijdias/article/view/2247
Whig, P., & Bhatia, A. B. (2023). Interpretable Analysis of the Potential Impact of Various Versions of Corona Virus: A Case Study. In Explainable Artificial Intelligence for Biomedical Applications (pp. 57-78). River Publishers.
Whig, P (2023)AI-Driven Image Processing for Sustainable Development through Machine Learning in Environmental Conservation and Resource Management. (2023). International Journal of Sustainable Development Through AI, ML and IoT, 2(2), 1-10. https://ijsdai.com/index.php/IJSDAI/article/view/27
Kasula, B. Y. (2016). Advancements and Applications of Artificial Intelligence: A Comprehensive Review. International Journal of Statistical Computation and Simulation, 8(1), 1–7. Retrieved from https://journals.threws.com/index.php/IJSCS/article/view/214
Kasula, B. Y. (2017). Machine Learning Unleashed: Innovations, Applications, and Impact Across Industries. International Transactions in Artificial Intelligence, 1(1), 1–7. Retrieved from https://isjr.co.in/index.php/ITAI/article/view/169
Ronak Pansara, Master Data Management Importance in Today’s Organization, International Journal of Management (IJM), 12(10), 2021, pp. 55-59. https://iaeme.com/Home/issue/IJM?Volume=12&Issue=10
Pansara, R. (2023). Cultivating Data Quality to Strategies, Challenges, and Impact on Decision-Making. International Journal of Managment Education for Sustainable Development, 6(6), 24-33.
Pansara, R. (2023). Seeding the Future by Exploring Innovation and Absorptive Capacity in Agriculture 4.0 and Agtechs. International Journal of Sustainable Development in Computing Science, 5(2), 46-59.
Pansara, R. (2023). Unraveling the Complexities of Data Governance with Strategies, Challenges, and Future Directions. Transactions on Latest Trends in IoT, 6(6), 46-56.
Kasula, B. Y. (2017). Transformative Applications of Artificial Intelligence in Healthcare: A Comprehensive Review. International Journal of Statistical Computation and Simulation, 9(1). Retrieved from https://journals.threws.com/index.php/IJSCS/article/view/215
Kasula, B. Y. (2018). Exploring the Efficacy of Neural Networks in Pattern Recognition: A Comprehensive Review. International Transactions in Artificial Intelligence, 2(2), 1–7. Retrieved from https://isjr.co.in/index.php/ITAI/article/view/170
Kasula, B. Y. (2019). Exploring the Foundations and Practical Applications of Statistical Learning. International Transactions in Machine Learning, 1(1), 1–8. Retrieved from https://isjr.co.in/index.php/ITML/article/view/176
Kasula, B. Y. (2019). Enhancing Classification Precision: Exploring the Power of Support-Vector Networks in Machine Learning. International Scientific Journal for Research, 1(1). Retrieved from https://isjr.co.in/index.php/ISJR/article/view/171
Whig, P., Velu, A., Nadikattu, R. R., & Alkali, Y. J. (2024). Role of AI and IoT in Intelligent Transportation. In Artificial Intelligence for Future Intelligent Transportation (pp. 199-220). Apple Academic Press.
Whig, P., Velu, A., Nadikattu, R. R., & Sharma, P. (2024). Reinforcement Learning for Automated Medical Diagnosis and Dynamic Clinical Regimes. In Handbook of Research on Artificial Intelligence and Soft Computing Techniques in Personalized Healthcare Services (pp. 169-187). Apple Academic Press.