Enhanced E-commerce Customer Engagement: A Comprehensive Three-Tiered Recommendation System

Authors

  • Kexin Wu Independent researcher Author
  • Kun Chi Author

DOI:

https://doi.org/10.60087/jklst.vol2.n2.p359

Keywords:

Machine Learning, KNN, SVD, Recommendation Systems, K-Means

Abstract

This paper delves into a sophisticated, multi-faceted recommendation system designed for e-commerce businesses. Its primary aim is to enrich customer experience and foster loyalty by employing three distinct, tailored recommendation strategies. Each module is designed to cater to different phases of the customer's e-commerce journey: the first focuses on first-time visitors, the second on users with a purchase history, and the third aids businesses new to e-commerce. The system's comprehensive nature allows it to offer relevant, personalized recommendations, significantly improving customer engagement and retention.

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Published

2024-01-27

How to Cite

Wu, K., & Chi, K. (2024). Enhanced E-commerce Customer Engagement: A Comprehensive Three-Tiered Recommendation System. Journal of Knowledge Learning and Science Technology ISSN: 2959-6386 (online), 2(3), 348-359. https://doi.org/10.60087/jklst.vol2.n2.p359

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