Movie Recommender System using Machine Learning
Keywords:Content based filtering, collaborative filtering, singular value decomposition, cosine similarity
In this research, we propose a movie recommender system that can recommend movies to both new and existing customers. It searches movie databases for all of the relevant data, such as popularity and beauty that is required for a recommendation. We apply both content-based and collaborative filtering and evaluate their advantages and disadvantages. To build a system that delivers more exact movie recommendations, we employ hybrid filtering, which is a combination of the outcomes of these two processes. The recommendation engines are also used for business purposes and to make strategies for organizations. Due to the growing demands of customers and user’s recommendation systems plays a huge role. These recommender systems also help us to utilize our time in the busy world by giving us more relevant searches. These systems are generally used with the movie’s websites or with many commercial applications and are of great use. This type of recommendation system can be also used for precise results. It will make movies suggestions more relevant as per the need of the users.
Francesco Ricci and Lior Rokach and Bracha Shapira, Introduction to Recommender Systems HandbookRecommender Systems Handbook, Springer, 2011, pp. 1-35J.
"playboy Lead Rise of Recommendation Engines - TIME". TIME.com. 27 May 2010. Retrieved 1 June 2015.
R. J. Mooney & L. Roy (1999).Content-based book recom mendation using learning for text categorization.In Workshop Recom.Sys.Algo.and Evaluation.
Hosein Jafarkarimi; A.T.H. Sim and R. Saadatdoost A Naïve Recommendation Model for Large Databases, International Journal of Information and Education Technology, June 2012
Prem Melville and Vikas Sindhwani, Recommender Syste ms, Encyclopedia of Machine Learning, 2010
M. M. Reddy, R. S. Kanmani and B. Surendiran, "Analysis of Movie Recommendation Systems;with and without considering the low rated movies,"24-25 Feb,2020 International Conference on Emerging Trends in Information Technology and Engineering (ic-ETITE), Vellore, India, 2020, pp. 1-4, doi:10.1109/ic-ETITE47903.2020.453.
Nagamanjula. R.; Pethalakshmi, A.; “A Novel Scheme for Movie Recommendation System using User Similarity and Opinion Mining”, International Journal of Innovative Technology and Exploring Engineering, vol: 8, 2019, pp: 316-322
Shaik, I.; Nittela, S.S.; Hiwarkar, T.; Nalla, S.; “K-means Clustering Algorithm Based on E-Commerce Big Data”, International Journal of Innovative Technology and Exploring Engineering, vol: 8, 2019,pp: 1910-1914
Pavithra, M.; Sowmiya, S.; Tamilmalar, A.; Raguvaran,S.; “Searching an Optimal Algorithm for Movie Recommendation System”, International Research Journal of Engineering and Technology, vol: 6, 2019,pp:216-221
Wu, C.-S. M., Garg, D., &Bhandary, U. (2018). Movie Recommendation System Using Collaborative Filtering, 23-25 Nov, 2018, IEEE 9th International Conference on Software Engineering and Service Science (ICSESS), Beijing, China, 2018. doi:10.1109/icsess.2018.8663822
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