Movies are one kind of enjoyment, however the challenge is locating the specified resources among the lots of movies discharged annually. To search out a show what users are seeking out through this technology is incredibly troublesome. However, recommendation structures return abundant handier in these things. Searching is the first step depending on which the movies are recommended to the user. To search a movie the user may use any of the parameter like name of the actor, director, genres etc. Searching based on one of the parameter and recommending movies based on the parameter is how the current recommendation system works. The movies are recommended with the help of filtering technique i,e collaborative filtering which recommends the movies based on user preference and ratings of the movies. This can be further enhanced by taking multiple parameters (2 in this case) for searching the movies based on which the recommendation can be done. With the help of multiple parameters (2 in this case) the user can be more specific in what he wants to search so that it helps in more accurate recommendations of the movies.
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Author(s) thanks to Dr.Ashwini KB for this research completion and Data validation support.
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Onkar N Jadhav
Onkar N Jadhav
R V College of Engineering, Bengaluru, Karnataka, India.
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Onkar N Jadhav, Ashwini KB, “Movie Recommendation System Using Machine Learning Algorithms”, Journal of Machine and Computing, vol.2, no.2, pp. 081-086, April 2022. doi: 10.53759/7669/jmc202202011.
