Analysis of The Impact of Deep Learning-Based Recommendation Algorithms on Demographic Groups

DeGroote School of Business, McMaster University, Hamilton, Ontario, L8S 4L8, Canada

niuj25@mcmaster.ca

Abstract

This study compares the performance of TensorFlow Recommender (TFRS), Light Factorization Machine (LightFM), and Weighted Matrix Factorization (WMF) on the MovieLens 25M dataset. It focuses on accuracy and fairness across different user groups. Experiments show that TFRS achieves good accuracy and keeps fairness across gender and age, but its performance drops sharply in sparse environments. LightFM performs better in cold-start cases but shows large gaps in fairness, especially among older users. WMF shows the most consistent fairness across age and gender groups because it uses confidence-weighted feedback methods, though its accuracy is lower. In controlled tests, TFRS ranks first in recommendation accuracy, WMF ranks first in exposure balance, and LightFM ranks first in new user adaptability. These results show that each model has strengths depending on the deployment environment. TFRS is good for mobile apps with quick user updates, WMF suits systems with high fairness needs, and LightFM is good when handling new users.