Movie Recommendation System
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Movie Recommendation System
Prof. Sonali Guhe
Asst. Prof Dept of Information Technology G.H.Raisoni College of Engineering, Nagpur.
| Shikhar Agrawal | Shreyash Bhagde | Saurabh Sawai | Sonali Guhe |
| Information Technology | Information Technology | Information Technology | Asst. Professor |
| G.H.R.C.E | G.H.R.C.E | G.H.R.C.E | G.H.R.C.E |
| shikhar.agrawal.it@ghrce.raisoni.net | shreyash.bhagde.it@ghrce.raisoni.net | saurabh.sawai.it@ghrce.raisoni.net | sonali.guhe.@.raisoni.net |
Abstract – The abundance of digital media and streaming services has led to a growing demand for personalized movie recommendations.
A movie recommendation system using matrix coefficients has become an effective solution to this problem. This research paper discusses the concept of matrix analysis and how it can be used to create an effective movie recommendation system. We focus on singular value decomposition (SVD), which is the most commonly used method for movie recommendation systems. We also discuss the challenges in building a movie recommendation system using matrix coefficients, such as managing the scarcity of user movie rating matrices. We provide a comprehensive overview of the movie recommendation system, including data collection, preprocessing, and ratings. We conclude that matrix factorization is an effective method for building movie recommendation system and its potential in other applications.
Keyword:movie recommendation ; Matrix Factorization; Singular Value Decomposition (SVD
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