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

Neelima Jain and Abid Hussain

International Journal of Scientific Research in Science and Technology, 2024, vol. 11, issue 6, 1092-1105

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.

Keywords: Recommendation System; Content-Based Filtering; Collaborative Filtering; Hybrid Filtering; Python; Tensorflow; Lightfm; Scikit; Matplotlib; Evaluation Metrics (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://ijsrst.com/home/article/view/IJSRST251263 Abstract page (text/html)
https://ijsrst.com/home/article/download/IJSRST251263/IJSRST251263 Full text (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:etm:ijsrst:v11:y2024:i6:id:980

DOI: 10.32628/IJSRST251263

Access Statistics for this article

More articles in International Journal of Scientific Research in Science and Technology from Technoscience Academy
Bibliographic data for series maintained by Pankaj Sharma ().