CollaborativeFiltering

For our project, we have taken reference from the github. This project is done by a PhD student at Université de la Polynésie Française.

We have tested our project on googlecolab.
For user based: https://colab.research.google.com/drive/1NImjLB1uaIKhi94JgiKsWnovLvDVYCvJ?usp=sharing

For item based: https://colab.research.google.com/drive/1ZBEn06A74zOyfakIppsHCpQ6UEoYzUR3?usp=sharing

Reuqired Libraries

• matplotlib==3.2.2
• numpy==1.19.2
• pandas==1.0.5
• python==3.7
• scikit-learn==0.24.1
• scikit-surprise==1.1.1
• scipy==1.6.2

Dataset

We have user Movielens dataset

Movielens 100K Dataset

It has 100,000 ratings (1-5) from 943 users on 1682 movies.

Ratings per user

min = 20 mean = 106 max = 737

Ratings per movie

min  =   1     mean =  59    max   = 583

Reference:

softcm, n. (2021, April 18). GitHub - nzhinusoftcm/review-on-collaborative-filtering: This repository presents a comprehensive implementation of collaborative filtering recommender systems. GitHub. https://github.com/nzhinusoftcm/review-on-collaborative-filtering

Movielens 100K dataset https://grouplens.org/datasets/movielens/100k/

Movielens 1M dataset. https://grouplens.org/datasets/movielens/1m/