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Designed a movie recommendation system using content-based, collaborative filtering based, SVD and popularity based approach.

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Movie-Recommendation-System

Dataset used:

  1. MovieLens
  2. The Movie Database: tmdb

Aim: Build a movie recommendation system by integrating the aspects of personalization of user with the overall features of movie such as genre, popularity etc.

Models:

  • Popularity model
  • Content based model: genre, year of release, ratings of movies
  • Collaborative filtering: User vs item, KNN similarity measures
  • Latent Factor based SVD
  • Combined linear model using surprise library (CF + SVD)
  • Hybrid model (content based + popularity based + item-item CF + svd)

Results:

Hybrid model

All the models are implemented in Python using pandas, sklearn and surprise library. The hyperparameter tuning, testing accuracy (RMSE and MAE) and evaluation of recommendations (precision, recall, f-measure and ndcg) for each model are thoroughly performed. The detailed analysis of the models is presented in the report.