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