ms-heidari / Recommender-System-with-Non-Negative-Matrix-Factorization

Project for Machine Learning Course in Shahid Beheshti University

Recommender System with Non Negative Matrix Factorization

Computer Science Department of Shahid Beheshti University

Machine Learning Course Project

Student : Mohammad Saeid Heidari

Supervisor : Dr.Katanforoush

February 2023

About the project

In this project, I have implemented the recommender system with two methods, 1-Non Negative Matrix Factorization and 2-Ordinal Non Negative Matrix Factorization, And every time I have run it on Netflix Prize dataset and reported the result, it is interesting to examine the error history of two matrix W and H in both methods.

Dataset

The file "training_set.tar" is a tar of a directory containing 17770 files, one per movie. The first line of each file contains the movie id followed by a colon. Each subsequent line in the file corresponds to a rating from a customer and its date in the following format:

CustomerID,Rating,Date

• MovieIDs range from 1 to 17770 sequentially.
• CustomerIDs range from 1 to 2649429, with gaps. There are 480189 users.
• Ratings are on a five star (integral) scale from 1 to 5.
• Dates have the format YYYY-MM-DD.

References

• Ordinal Non-negative Matrix Factorization for Recommendation (main paper)
• Algorithms for Non-negative Matrix Factorization
• Bayesian Non-negative Matrix Factorization