In this project, I created a Hybrid Recommender System which makes predictions using item-based and user-based recommender methods for the user with the given ID. It considers five recommendations from the user-based model and five recommendations from the item-based model, and ultimately provides ten recommendations from both models.
The datasets describe ratings and free-text tagging activities from MovieLens, a movie recommendation service. It contains 20000263 ratings and 465564 tag applications across 27278 movies. These data were created by 138493 users between January 09, 1995 and March 31, 2015. This dataset was generated on October 17, 2016.
Users were selected at random for inclusion. All selected users had rated at least 20 movies.
Dataset link:
https://www.kaggle.com/datasets/grouplens/movielens-20m-datasetThis project completed as a part of Miuul Data Science & Machine Learning Bootcamp.