Machine Learning-based Movie Recommendation Engine

Authors: Sonu Airen, Puja Gupta

Country: India

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Abstract: We propose a movie recommendation engine that can offer films to both new and existing customers as part of this study. The system searches movie databases to gather relevant information, including popularity and beauty, needed to make a recommendation. We use content-based filtering and collaborative filtering, and we evaluate the advantages and disadvantages of each strategy. We use hybrid filtering, combining the outcomes of two algorithms to create a system that offers more precise movie recommendations. Recommendation engines are used for commercial purposes and aid in developing strategies for businesses. Recommendation systems are crucial due to the growing demands of customers and user suggestions. Recommender systems help us find more relevant searches, improving our time management in a busy workplace. It is typical to use these systems alongside movie websites or other business apps due to their high utility. Particular results may be achieved with the use of this kind of recommendation system. Movie suggestions will become more tailored to customers' needs as a consequence of this.

Keywords: Recommendation System, Machine Learning, content based filtering

Paper Id: 230496

Published On: 2022-01-05

Published In: Volume 10, Issue 1, January-February 2022

Cite This: Machine Learning-based Movie Recommendation Engine - Sonu Airen, Puja Gupta - IJIRMPS Volume 10, Issue 1, January-February 2022.

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