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A web-based movie recommender using unsupervised learning to suggest movies based on user input. Recommendations through scikit-learn NMF and CosineSimilarity.

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POPCORN

A Movie Recommender built with a web interface. This project entails a web-based movie recommender engine using two different recommendation models: NMF (Negative Matrix Factorization algorithm) and an User-based Cosimilarity Matrix recommender algorithm that takes the top 10 most similar users and creates a new movie vector from their ratings average to recommend movies that would most likely be appreciated by the user giving the rating inputs. This is a collaborative project, and it is still in development.

Link:

http://novi.pythonanywhere.com/

Portuguese-BR version of the web-app, deployed

Models:

1. NMF: Negative matrix factorization

2. Cosim: User-based cosimilarity matrix model. This option creates a new user with ratings of the given movies (all consider 5) and recommend movies from similar user ratings

Tech used:

  • Python
  • Flask
  • HTML
  • CSS
  • PostgreSQL
  • sqlalchemy
  • Scikit-learn

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A web-based movie recommender using unsupervised learning to suggest movies based on user input. Recommendations through scikit-learn NMF and CosineSimilarity.

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