Comparative Analysis of Matrix Factorization and Neural Collaborative Filtering for Movie Recommendation Systems

Author(s)

• Mahyar AlinejadFollow

Keywords

Recommendation Systems, Matrix Factorization, Neural Collaborative Filtering, MovieLens Dataset, Predictive Accuracy

Description

This paper presents a comparative study of two recommendation system approaches for predicting movie ratings: Matrix Factorization with Stochastic Gradient Descent (SGD) optimization and Neural Collaborative Filtering (NCF) using Tensor Flow. The study aims to evaluate the effectiveness of these methods in recommending movies to users based on the MovieLens 100K dataset. The Matrix Factorization approach utilizes latent features to model user preferences and item characteristics, optimizing parameters through SGD. On the other hand, NCF integrates traditional collaborative filtering with neural networks to capture complex user-item interactions. Experimental results demonstrate the performance of both models in terms of Root Mean Square Error (RMSE) on the test dataset. The findings provide insights into the strengths and limitations of each approach, aiding in the selection of suitable recommendation techniques for movie recommendation systems.

Abstract

This paper presents a comparative study of two recommendation system approaches for predicting movie ratings: Matrix Factorization with Stochastic Gradient Descent (SGD) optimization and Neural Collaborative Filtering (NCF) using Tensor Flow. The study aims to evaluate the effectiveness of these methods in recommending movies to users based on the MovieLens 100K dataset. The Matrix Factorization approach utilizes latent features to model user preferences and item characteristics, optimizing parameters through SGD. On the other hand, NCF integrates traditional collaborative filtering with neural networks to capture complex user-item interactions. Experimental results demonstrate the performance of both models in terms of Root Mean Square Error (RMSE) on the test dataset. The findings provide insights into the strengths and limitations of each approach, aiding in the selection of suitable recommendation techniques for movie recommendation systems.

Instructor Name

Dr rui Xie

STARS Citation

Alinejad, Mahyar, "Comparative Analysis of Matrix Factorization and Neural Collaborative Filtering for Movie Recommendation Systems" (2025). Data Science and Data Mining. 44.
https://stars.library.ucf.edu/data-science-mining/44