A Hybrid Machine Learning Approach for Improving E-Commerce Recommendation Systems Using Python
Year: 2024
Type: article
Abstract: This paper presents a novel hybrid recommendation system approach that combines collaborative filtering, content-based filtering, and deep learning techniques to improve recommendation accuracy and overcome common challenges in e-commerce platforms. Our proposed model addresses key limitations such as the cold-start problem, data sparsity, and overspecialization by leveraging the complementary strengths of multiple recommendation strategies. Implementation using Python demonstrates significant performance improvements across various evaluation metrics compared to standalone methods, providing a practical framework for e-commerce recommendation systems.
Authors: Neelima Jain, Dr. Abid Hussain
Institution: Career Point University
Language: English
Subfield: Information Systems
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4412059112