Abstract
These days, recommendation engines are so pervasive that many of us are not even aware of them. A recommendation system is essential for a better user experience because no one could possibly read through all of a website’s offerings or information. Additionally, it makes more inventories visible that would otherwise be hidden. Amazon’s review sites, Netflix’s proposals for shows and movies in your newsfeed, YouTube’s suggested videos, Spotify’s suggested music, Instagram’s newsfeed, and Google AdWords are all examples of recommender systems in use. This study suggests a Python-based machine learning predictive analysis-based intelligent movie recommendation system (RS). In this study, RS uses the correlation between numbers of factors to obtain precise results. According to the simulation results, RS has improved in terms of content and data similarity from users. The purpose of this study is to acquire the skills necessary to carry out feature engineering, handle missing values, manipulate data in accordance with specifications on real-time data, and suggest appropriate things specific content and likeness.
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Verma, P., Gupta, P., Singh, V. (2023). A Smart Movie Recommendation System Using Machine Learning Predictive Analysis. 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_4
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DOI: https://doi.org/10.1007/978-981-19-7615-5_4
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