Hybrid Recommender System

This repo contains the following recommender systems.

• Association Rule Learning Recommender
• Content Based Recommender
• Item Based Recommender
• User Based Recommender
• Matrix Factorization Recommender

Contains a project that is a hybrid method of the above methods.

Business Problem

For the user whose ID is given, it is desired to make 10 movie recommendations using item-based and user-based recommender methods

Dataset Info

movie.csv

Feature	Definition
movieId	Unique movie ID
title	Movie title
genres	Movie genre

rating.csv

userId	Unique User ID
movieId	Unique Movie ID
rating	Rating given to the movie by the user
timestamp	Review data

Requirements

mlxtend==0.21.0
pandas==1.4.4
scikit_learn==1.1.2
scikit_surprise==1.1.2
surprise==0.1

Files

01_arl.ipynb - Association Rule Learning Notebook

02_content_based_recommender.ipynb - Content Based Filtering Movie Recommender

03_item_based_recommender.ipynb - Item Based Filtering Movie Recommender

04_user_based_recommender.ipynb - User Based Filtering Movie Recommender

05_matrix_factorization.ipynb - Matrix Factorization Movie Recommender

06_Hybrid-Recommender-System-Project.ipynb - Hybrid Movie Recommender PROJECT

Author

Oğuz Erdoğan