Abstract
The entertainment industry is booming, and machine learning is playing a vital role in the technical world. Content consumption habits are growing more complicated and evolving at a faster rate than ever before. Machine learning-based recommendation systems forms self-sufficient system which learn from their experiences and improve without having to be explicitly coded. It is a mechanism that allows a user to find information that is relevant to him or her from huge amounts of data. Every entertainment company uses a complex recommendation algorithm to display meaningful content to a user based on his preferences. It helps them to increase their sales and retain their user base. Movie recommendations systems have various approaches such as collaborative filtering (CF) which compares users for similarity of content consumption or content-based filtering which uses the movie’s features such as year of release, genre, and actors. A hybrid approach incorporates two or more different approaches of movie recommendation. We present a solution in this paper of movie recommendation system architecture that uses MERN stack and ML and handles the cold-start problem.
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Gupta, S., Rawat, D., Gupta, K., Yadav, A.K., Gandhi, R., Gupta, A. (2023). Movie Recommendation System Using Machine Learning and MERN Stack. In: Khanna, A., Polkowski, Z., Castillo, O. (eds) Proceedings of Data Analytics and Management . Lecture Notes in Networks and Systems, vol 572. Springer, Singapore. https://doi.org/10.1007/978-981-19-7615-5_20
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DOI: https://doi.org/10.1007/978-981-19-7615-5_20
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