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contentbasedfiltering

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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.

  • Updated May 2, 2022
  • Python

System is going to filter out the best possible movies basis on some criteria in recommendation area even after analyzing and previewing the reviews of the particular movie using sentiment analysis theory.

  • Updated Sep 21, 2021
  • Jupyter Notebook

The application uses content based filtering to make recommendations. For every movie selected, 12 recommendations are made based on their cosine similarity with the selected movie. An API feteches the poster image of the movie and displays them in an image grid to the user The database offers nearly 5000 movies to select from

  • Updated Dec 11, 2022
  • Python

The Movie Recommendation System is a Python application that provides personalized movie suggestions using collaborative and content-based filtering techniques. Utilizing the MovieLens 25M dataset, it offers customizable recommendations based on user ID, movie title, and desired suggestion count, creating an engaging and tailored movie discovery.

  • Updated Apr 4, 2023
  • Jupyter Notebook

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