Cross-Border E-Commerce Intelligent Information Recommendation System Based on Deep Learning

Area of Science:

• Artificial Intelligence
• E-commerce Technology
• Information Retrieval

Background:

• Cross-border e-commerce faces challenges in intelligent information processing and recommendation.
• Effective feature extraction and handling translation issues are crucial for recommendation systems.
• Discovering and recommending niche products is vital for expanding market reach.

Purpose of the Study:

• To improve the effectiveness of intelligent information recommendation in cross-border e-commerce.
• To address feature extraction limitations in text data for recommendation systems.
• To leverage long-tail theory for niche product discovery and recommendation.

Main Methods:

• Application of deep learning techniques for intelligent information processing.
• Development of an improved topic model for enhanced feature extraction.
• Implementation of an end-to-end sequence-to-sequence learning method for translation.
• Utilization of long-tail theory and graph-based models for niche product recommendation.

Main Results:

• The proposed deep learning-based system demonstrates a significant improvement in recommendation effectiveness.
• The improved topic model successfully addresses feature extraction challenges.
• The sequence-to-sequence model effectively handles translation issues in cross-border e-commerce.
• The niche product recommendation algorithm successfully identifies and suggests relevant long-tail items.

Conclusions:

• Deep learning provides a robust framework for enhancing cross-border e-commerce recommendation systems.
• The integrated approach effectively tackles feature extraction, translation, and niche product discovery.
• The developed system meets the complex recommendation needs of the cross-border e-commerce landscape.