Hybrid Recommendation System in E-Commerce

Rey Muhamad Rifqi; Djupriadi; Widodo Tri Haryanto; Ema Utami; Alva Hendi Muhamad

doi:10.59890/ijist.v3i5.29

Authors

Rey Muhamad Rifqi University of Amikom Yogyakarta
Djupriadi University of Amikom Yogyakarta
Widodo Tri Haryanto University of Amikom Yogyakarta
Ema Utami University of Amikom Yogyakarta
Alva Hendi Muhamad University of Amikom Yogyakarta

DOI:

https://doi.org/10.59890/ijist.v3i5.29

Keywords:

Recommendation, Hybrid, Recommendation System, Content-Based, Collaborative Filtering

Abstract

This study explores hybrid recommendation systems in e-commerce, which combine content-based and collaborative filtering to overcome limitations such as cold-start and data sparsity. Through a Systematic Literature Review of 15 selected papers, it identifies key hybrid types—Weighted, Switching, and Cascade Hybridization—and analyzes trends in their adoption. Weighted Hybridization is found to be the most frequently used due to its effectiveness in improving recommendation accuracy. The study also discusses the strengths of hybrid systems in providing personalized and adaptive suggestions, along with challenges like system complexity and computational cost. Overall, hybrid approaches offer promising improvements for user experience in e-commerce recommendation systems.

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