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A Hybrid Machine Learning Approach for Improving E-Commerce Recommendation Systems Using Python

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  • Neelima Jain
  • Abid Hussain

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.

Suggested Citation

  • Neelima Jain & Abid Hussain, 2024. "A Hybrid Machine Learning Approach for Improving E-Commerce Recommendation Systems Using Python," International Journal of Scientific Research in Science and Technology, Technoscience Academy, vol. 11(6), pages 1092-1105, December.
    • Handle: RePEc:etm:ijsrst:v11:y2024:i6:id:980
    DOI: 10.32628/IJSRST251263
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