Dataset used:
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.