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This repository covers a project of creating a recommendation system using collaborative filtering on the Grouplens movielens database. The surprise library is utilized to test out different models (KNN Basic, KNN Baseline, and SVD). SVD was found to be the most accurate and then was implemented into the system. The cold start problem was addressed by giving new users the opportunity to rate a random sample of 5 movies from movies that are among the most popular.

Home Page: https://roweyerboat.github.io/the_helpful_library_of_surprise

Jupyter Notebook 97.97% Python 2.03%
movielens ratings users-ratings movielens-data surprise-library surprise-python movies

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