prakruti-joshi / Movie-Recommendation-System

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

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:

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.