B. Sriram,
- PG Student, Department of Artificial Intelligence and Data Science, Sri Manakula Vinayagar College of Engineering and Technology, Puducherry, India
Abstract
No matter their age, gender, race, color, or region, everyone enjoys watching films particularly during festival season. We are all, in the most basic sense, connected to one another through this beautiful medium, but what really grabs attention is the fact that, regardless of how unique our choices and combinations are in terms of picture show preference, one thing remains constant. Certain people have a preference for certain types of movies, such as romance, sci-fi, or thrillers, while others focus on lead actors and directors. Having said that, a certain segment of the public continues to enjoy seeing such films. In this paper, the recommendation towards seasonal-based shows, such as Christmas eve, New Year, Valentine’s Day, Independence Day, Halloween, etc., are discussed using hybrid approach with user interest in various OTT (over-the-top) platforms.
Keywords: Recommendation system, seasonal, hybrid approach, over-the-top (OTT) platform, Machine learning, CBF algorithms
[This article belongs to Journal of Multimedia Technology & Recent Advancements (jomtra)]
B. Sriram. A Survey of Seasonal-based Movie Recommendations Using Machine Learning Through a Hybrid Approach with User Interest in Various OTT Platforms. Journal of Multimedia Technology & Recent Advancements. 2024; 11(01):24-29.
B. Sriram. A Survey of Seasonal-based Movie Recommendations Using Machine Learning Through a Hybrid Approach with User Interest in Various OTT Platforms. Journal of Multimedia Technology & Recent Advancements. 2024; 11(01):24-29. Available from: https://journals.stmjournals.com/jomtra/article=2024/view=138591
References
- Sharma N, Dutta M. Movie recommendation systems: a brief overview. In: Proceedings of the 8th International Conference on Computer and Communications Management, Singapore, July 17–19, 2020. pp. 59–62.
- Singh RH, Maurya S, Tripathi T, Narula T, Srivastav G. Movie recommendation system using cosine similarity and KNN. Int J Eng Adv Technol. 2020; 9 (5): 556–559.
- Kumar M, Yadav DK, Singh A, Gupta VK. A movie recommender system: MOVREC. Int J Computer Appl. 2015; 124 (3): 7–11.
- Kashyap A, Sunita B, Srivastava S, Aishwarya PH, Shah AJ. A movie recommender system: MOVREC using machine learning techniques. Int J Eng Sci Comput. 2020; 10 (6): 26195–26200.
- Tejaswi GV, Krishna SR, Madhuri GL, Prasanna C. A framework to enhance the movie recommendation system by using data mining. Iconic Res Eng J. 2021; 5 (3): 47–60.
- Nakhli RE, Moradi H, Sadeghi MA. Movie recommender system based on percentage of view. In: 2019 5th Conference on Knowledge Based Engineering and Innovation (KBEI), Tehran, Iran, February 28–March 1, 2019. pp. 656–660.
- Wang G, Liu H. Survey of personalized recommendation system. Jisuanji Gongcheng yu Yingyong–Computer Eng Appl. 2012; 48 (7): 66–76.
- Peng X, Liangshan S, Xiuran L. Improved collaborative filtering algorithm in the research and application of personalized movie recommendations. In: 2013 Fourth International Conference on Intelligent Systems Design and Engineering Applications, Zhangjiajie, China, November 6–7, 2013. pp. 349–352.
- Wu CSM, Garg D, Bhandary U. Movie recommendation system using collaborative filtering. In: 2018 IEEE 9th International Conference on Software Engineering and Service Science (ICSESS), Beijing, China, November 23–25, 2018. pp. 11–15.
- Sharma M, Mann S. A survey of recommender systems: approaches and limitations. Int J Innov Eng Technol. 2013; 2 (2): 8–14.
- Agrawal S, Jain P. An improved approach for movie recommendation system. In: 2017 International Conference on IoT in Social, Mobile, Analytics and Cloud (I-SMAC), Palladam, India, February 10–11, 2017. pp. 336–342.
- Nagamanjula R, Pethalakshmi A. Novel scheme for movie recommendation system using user similarity and opinion mining: a recent study. Recent Dev Eng Res 2021; 12: 138–148.
- Jahrer M, Töscher A, Legenstein R. Combining predictions for accurate recommender systems. In: Proceedings of the 16th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Washington, DC, USA, July 25–28, 2010. pp. 693–702.
- Ghai B, Dhar J, Shukla A. Multi-level ensemble learning based recommender system. Corpus ID. 2018: 28239124.
- Recommender System [Online]. Available at https://en.wikipedia.org/wiki/Recommender_system
- Thorave S, Sharma A, Nair S, Shinde T, Tipras M. Survey on movie recommendation system using machine learning. Int Res J Modern Eng Technol Sci. 2022; 4 (12): 1182–1185.
Journal of Multimedia Technology & Recent Advancements
Volume | 11 |
Issue | 01 |
Received | 16/02/2024 |
Accepted | 27/02/2024 |
Published | 04/04/2024 |