Matrix Factorization, Recommendation System, Movie


Recommendation systems are a popular and beneficial field that can help people make informed decisions automatically. This technique assists users in selecting relevant information from an overwhelming amount of available data. When it comes to movie recommendations, two common methods are collaborative filtering, which compares similarities between users, and content-based filtering, which takes a user’s specific preferences into account. However, our study focuses on the collaborative filtering approach, specifically matrix factorization. Various similarity metrics are used to identify user similarities for recommendation purposes. Our project aims to predict movie ratings for unwatched movies using the MovieLens rating dataset. We developed a model that uses a matrix factorization algorithm to predict ratings and recommends movies with the highest predicted ratings to users. The achieved values of RMSE were relatively low, indicating that the system was able to provide accurate recommendations for users.

Course Name

STA 6704 Data Mining 2

Instructor Name

Dr. Rui Xie


College of Sciences