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Content Based Recommendation system uses attributes of the content to recommend similar content. It doesn't have a cold-start problem because it works through attributes or tags of the content, such as actors, genres or directors, so that new movies can be recommended right away.
Content-Based Recommender System recommends movies similar to the movie user likes. The details of the movies(title, genre, runtime, rating, poster, etc) are fetched using an API by TMDB.
Recommendation systems are among the most popular applications of data science. They are used to predict the Rating or Preference that a user would give to an item.
This project will recommend users the opportunity to watch movies similar to the one they have previously watched and if new movie is going to be released, its IMDb rating can be predicted.
Discover your next movie night with our Movie Recommender System! Explore personalized movie suggestions based on your preferences, powered by advanced recommendation algorithms.
A Movie Recommender System is an application program build using python programming that can recommend you the similar movies according to your search. Streamlit library is used for front-end development