E-Commerce Platform Recommendation Method and System Based on Multi-Algorithm Fusion

Wei YiXuan, Qu YanTong

2025

Abstract

In order to solve the challenges of e-commerce platform recommendation methods and systems, this study introduces an innovative platform recommendation method and system method based on multi-algorithm fusion in view of the shortcomings of the existing heap sorting algorithms. This new approach uses the principles of sub-problem theory to accurately identify and locate key influencing factors, and accordingly makes a wise classification of indicators to reduce possible interference. At the same time, using the unique mechanism of multi-algorithm fusion, the design strategy of the recommendation method is cleverly constructed in this scheme. The empirical results show that the proposed scheme shows a significant improvement compared with the traditional heap sorting algorithm in terms of key performance indicators such as the accuracy of the platform recommendation method and system, and the processing efficiency of key factors, showing its obvious strong advantages. In the e-commerce platform, the platform recommendation method and system play a vital role, which can accurately predict and optimize the growth trend and output results of the e-commerce platform recommendation method and system. However, in the face of complex simulation tasks, traditional heap sorting algorithms show some inherent shortcomings, especially when dealing with multi-level challenges, their performance is often unsatisfactory. To overcome this, this study introduces the platform recommendation method and new system ideas of multi-algorithm fusion optimization, and accurately controls the influencing parameters through the sub-problem theory, and uses this as the road map for index allocation, and then uses multi-algorithm fusion to innovate and construct a system scheme. The test results clearly point out that in the context of the evaluation criteria, the new scheme has been significantly optimized in terms of accuracy and processing speed for a variety of challenges, showing stronger performance superiority. Therefore, in the recommendation method and system of e-commerce platform, the simulation scheme based on multi-algorithm fusion successfully overcomes the shortcomings of the traditional heap sorting algorithm, and significantly improves the accuracy and operation efficiency of the simulation.

Paper Citation

in Harvard Style

YiXuan W. and YanTong Q. (2025). E-Commerce Platform Recommendation Method and System Based on Multi-Algorithm Fusion. In Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT; ISBN 978-989-758-763-4, SciTePress, pages 358-365. DOI: 10.5220/0013543600004664

in Bibtex Style

@conference{incoft25,
author={Wei YiXuan and Qu YanTong},
title={E-Commerce Platform Recommendation Method and System Based on Multi-Algorithm Fusion},
booktitle={Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT},
year={2025},
pages={358-365},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0013543600004664},
isbn={978-989-758-763-4},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 3rd International Conference on Futuristic Technology - Volume 1: INCOFT
TI - E-Commerce Platform Recommendation Method and System Based on Multi-Algorithm Fusion
SN - 978-989-758-763-4
AU - YiXuan W.
AU - YanTong Q.
PY - 2025
SP - 358
EP - 365
DO - 10.5220/0013543600004664
PB - SciTePress