Year: 2025
Type: article
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.
DOI: 10.61173/qj7tma45
Author: Jinghan Liang
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4407978064