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Related Experiment Video

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Applying an eMASS Customization Program as a Research Tool to Evaluate Consumer Benefits

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Published on: September 27, 2019

RETRACTED ARTICLE: Application of big data search based on collaborative filtering algorithm in cross-border

Xiaoli Wu¹, Zhihao Wu²

¹School of Foreign Studies, Yiwu Industrial and Commercial College, Yiwu, 322000 China.

|June 26, 2023

Summary

This summary is machine-generated.

This study introduces a collaborative filtering algorithm for cross-border e-commerce big data search. It enhances product recommendations by incorporating user preferences and weighted labels for improved decision-making.

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Area of Science:

Computer Science
E-commerce Technology
Data Science

Background:

Cross-border e-commerce is rapidly expanding, leading to massive data growth.
Existing big data search systems struggle with inaccurate user profiling and suboptimal product recommendations.
The increasing volume of information complicates user decision-making processes.

Purpose of the Study:

To develop an improved big data search system for cross-border e-commerce platforms.
To enhance the accuracy and relevance of product recommendations.
To address the challenges posed by large datasets in user decision support.

Main Methods:

Implementation of a big data search system utilizing a collaborative filtering algorithm.
Construction of user matrix labels with quantification of new user preferences.
Integration of a weighting concept into user preference labels for algorithm input.

Main Results:

The proposed collaborative filtering algorithm effectively processes big data for e-commerce.
The system demonstrates improved product recommendation capabilities compared to existing methods.
Weighted user preference labels enhance the collaborative filtering algorithm's performance.

Conclusions:

Collaborative filtering, enhanced with weighted user preference labels, is a viable solution for cross-border e-commerce big data search.
The developed system can significantly improve user experience by providing more accurate product recommendations.
This approach addresses key limitations in current e-commerce big data search and recommendation systems.