Implementation and optimization of recommendation systems

Abstract

This study focuses on the implementation and optimization of a recommendation system using deep learning-based collaborative filtering algorithms. The system utilizes user-item interactions from provided datasets to predict user ratings for items in a test set. We introduce a hybrid model that incorporates both collaborative filtering and matrix factorization techniques to enhance prediction accuracy. The collaborative filtering approach exploits similarities between user preferences, while the matrix factorization method decomposes the user-item matrix to capture latent features. The effectiveness of the system is evaluated through various metrics, including precision and recall, with results indicating substantial improvements in recommendation accuracy and system robustness.