Movies Recommendation

Movies Recommendation

Mr. Tirth Patel1, Mr. Manan Bhayani2, Mr. Kishan Kayadra3, Mr. Smit Chheta4, Asst. Prof. Ms. Manisha Vasava5

1,2,3,4 Research Scholar, Department of Information Technology, Krishna School of Emerging Technology & Applied Research, KPGU University, Varnama, Vadodara, Gujarat, India

5 Assistant Professor, Department of Information Technology, Krishna School of Emerging Technology & Applied Research, KPGU University. Varnama, Vadodara, Gujarat, India

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Abstract - This paper presents a comprehensive review of movie recommendation systems and their applications in the entertainment industry. The paper highlights the different techniques used in movie recommendation systems such as collaborative filtering, content-based filtering, and hybrid filtering. The strengths and weaknesses of each technique are discussed, along with their respective algorithms. The paper also explores the challenges faced by movie recommendation systems such as data sparsity, cold start problem, and user bias. Furthermore, the paper examines the current state-of-the-art movie recommendation systems, their performance evaluation metrics, and their implementation in real world scenarios. The practical application of these systems by popular online streaming platforms is also discussed. The study concludes that movie recommendation systems are essential for enhancing user experience and engagement in the entertainment industry, and future research in this field is crucial to improving the accuracy and effectiveness of these systems.

Key Words: Recommendation System, PyCharm, Jupiter Notebook, Artificial Intelligence & Machine Learning.