A Hybrid Machine Learning Approach for Improving E-Commerce Recommendation Systems Using Python (International Journal of Scientific Research in Science and Technology, 2024)
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.
Keywords: Recommendation System; Content-Based Filtering; Collaborative Filtering; Hybrid Filtering; Python; Tensorflow; Lightfm; Scikit; Matplotlib; Evaluation Metrics