Movie Recommender System using Machine Learning

Authors

  • Sonika Malik Maharaja Surajmal Institute of Technology, New Delhi, India

DOI:

https://doi.org/10.4108/eetct.v9i3.2712

Keywords:

Content based filtering, collaborative filtering, singular value decomposition, cosine similarity

Abstract

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.

References

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Published

11-10-2022

How to Cite

1.
Malik S. Movie Recommender System using Machine Learning. EAI Endorsed Trans Creat Tech [Internet]. 2022 Oct. 11 [cited 2022 Dec. 3];9(3):e3. Available from: https://publications.eai.eu/index.php/ct/article/view/2712