A novel analysis on movie recommendation using machine learning approach
DOI:
https://doi.org/10.56294/piii2025376Keywords:
Support Vector Machine (SVM), Naive Bayes , K-Nearest Neighbors AlgorithmAbstract
In today’s culture, technology plays a crucial role for any kind of recommendations. In this digital World, Algorithms are used in movie recommendation systems to offer films to users based on their watching history or ratings. With the advent of digital services and the massive quantities of information they accumulate on user preferences, these systems have grown in popularity in recent years. This system employs machine learning to do sentiment analysis on movie reviews to improve the user experience. This article also compares NB, SVM, and KNN on metrics such as Accuracy, Precision, Recall, and F1 Score. We present an overview of the many algorithms utilized in the creation of movie recommendation systems, encompassing text categorization, knowledge filtering, and hybrid techniques, in this review. We also examine the problems and limits of these algorithms, as well as possible future research objectives in this subject
References
[1] Katarya, R., & Verma, O. P. (2017). An effective collaborative movie recommender system with cuckoo search. Egyptian Informatics Journal, 18(2), 105-112.
[2] Nassar, N., Jafar, A., & Rahhal, Y. (2020). A novel deep multi-criteria collaborative filtering model for recommendation system. Knowledge-Based Systems, 187, 104811.
[3] Pavitha, N., Pungliya, V., Raut, A., Bhonsle, R., Purohit, A., Patel, A., & Shashidhar, R. (2022). Movie Recommendation and Sentiment Analysis Using Machine Learning. Global Transitions Proceedings.
[4] Zhang, C., Duan, X., Liu, F., Li, X., & Liu, S. (2022). Three-way naive Bayesian collaborative filtering recommendation model for smart city. Sustainable Cities and Society, 76, 103373.
[5] Kang, H., Yoo, S. J., & Han, D. (2012). Senti-lexicon and improved Naïve Bayes algorithms for sentiment analysis of restaurant reviews. Expert Systems with Applications, 39(5), 6000-6010.
[6] Gonzales, R. M. D., & Hargreaves, C. A. (2022). How can we use artificial intelligence for stock recommendation and risk management? A proposed decision support system. International Journal of Information Management Data Insights, 2(2), 100130.
[7] Wang, Z., Ren, G., Sun, M., Ren, J., & Jin, J. S. (2015). Ranking highlight level of movie clips: A template based adaptive kernel SVM method. Journal of Visual Languages & Computing, 27, 49-59..
[8] Min, S. H., & Han, I. (2005, July). Recommender systems using support vector machines. In International Conference on Web Engineering (pp. 387-393). Springer, Berlin, Heidelberg.
[9] Li, H., Cui, J., Shen, B., & Ma, J. (2016). An intelligent movie recommendation system through group-level sentiment analysis in microblogs. Neurocomputing, 210, 164-173.
[10] Cui, L., & Shi, Y. (2014). A method based on one-class SVM for news recommendation. Procedia Computer Science, 31, 281-290.
[11] Wu, C. S. M., Garg, D., & Bhandary, U. (2018, November). Movie recommendation system using collaborative filtering. In 2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS) (pp. 11-15). IEEE.
[12] Phorasim, P., & Yu, L. (2017). Movies recommendation system using collaborative filtering and k-means. International Journal of Advanced Computer Research, 7(29), 52.
[13] Ruotsalo, T., Weber, S., & Gajos, K. Z. (2022). Active tag recommendation for interactive entity search: Interaction effectiveness and retrieval performance. Information Processing & Management, 59(2), 102856.
[14] Marjanović, M., Kovačević, M., Bajat, B., & Voženílek, V. (2011). Landslide susceptibility assessment using SVM machine learning algorithm. Engineering Geology, 123(3), 225-234.
[15] Pradhan, A. (2012). Support vector machine-a survey. International Journal of Emerging Technology and Advanced Engineering, 2(8), 82-85.
[16] Feng, W., Sun, J., Zhang, L., Cao, C., & Yang, Q. (2016, December). A support vector machine based naive Bayes algorithm for spam filtering. In 2016 IEEE 35th International Performance Computing and Communications Conference (IPCCC) (pp. 1-8). IEEE.
[17] Peling, I. B. A., Arnawan, I. N., Arthawan, I. P. A., & Janardana, I. G. N. (2017). Implementation of Data Mining To Predict Period of Students Study Using Naive Bayes Algorithm. Int. J. Eng. Emerg. Technol, 2(1), 53.
[18] Rahat, A. M., Kahir, A., & Masum, A. K. M. (2019, November). Comparison of Naive Bayes and SVM Algorithm based on sentiment analysis using review dataset. In 2019 8th International Conference System Modeling and Advancement in Research Trends (SMART) (pp. 266-270). IEEE.
[19] Pop, I. (2006). An approach of the Naive Bayes classifier for the document classification. General Mathematics, 14(4), 135-138.
[20] Sathyadevan, S., Sarath, P. R., Athira, U., & Anjana, V. (2014, August). Improved document classification through enhanced Naive Bayes algorithm. In 2014 International Conference on Data Science & Engineering (ICDSE) (pp. 100-104). IEEE.
Downloads
Published
Issue
Section
License
Copyright (c) 2025 ASVSNM Manjusha, Pachigulla Ajay Kumar, Singireddy Sreevani, Chatradi Tejaswini, K.Padmanaban (Author)
This work is licensed under a Creative Commons Attribution 4.0 International License.
The article is distributed under the Creative Commons Attribution 4.0 License. Unless otherwise stated, associated published material is distributed under the same licence.
