Abstract: In a growing world, where internet has everything, literally everything which makes tedious to search each and everything over and over. Like movies, series, songs and much more. A recommendation system is a specialized system, made for a specific domain, which shows similar searches, result based on our search and query. We live in an era of OTT, which made it possible that we don’t need theaters and TV for movies and series altogether. The need of recommendation rose up quickly. To give recommendation, once a series or a movie is finished, based on what is just finished. Write a name of a series of movies in Google, scroll down, and in the right you will see “people also search for”. That shows how recommendation systems are important. In this project, an attempt is made to make a hybrid movie recommendation system, by combining two techniques, collaborative filtering and content-based filtering.
Keyword: Content-based filtering, collaborative filtering, nearest neighbors, user-item interaction matrix.
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DOI:
10.17148/IJARCCE.2021.10448
