Chandru-21/End-to-End-Movie-Recommendation-System-with-deployment-using-docker-and-kubernetes
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.
This project helps entertainment platforms or content curators automatically suggest movies to viewers. By taking a movie title as input, it identifies key attributes like genre, actors, and directors, and then outputs a list of similar movie recommendations. It's designed for someone managing a movie catalog who wants to offer personalized suggestions to users.
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Use this if you need a system to recommend movies based on their characteristics, especially if you're dealing with new content and want immediate suggestions without needing extensive user viewing history.
Not ideal if you require highly advanced, hyper-personalized recommendations that consider individual user behavior and preferences beyond just movie attributes.
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Python
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Last pushed
May 02, 2022
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