Optimization and Innovation of AI-Based E-Commerce Platform Recommendation System - Huang, Jiangnan (Journal of Computer, Signal, and System Research, 2025)
With the rapid advancement of artificial intelligence (AI) technology, recommendation systems on e-commerce platforms have become essential tools for enhancing user experience and optimizing sales strategies. This study systematically investigates the optimization and innovation of AI-based recommendation systems, identifying key challenges in data quality, algorithmic accuracy, and system adaptability in current platforms. To address these challenges, we propose integrated strategies involving big data analytics, deep learning models, and data fusion techniques to improve the precision and personalization of recommendations. Additionally, the exploration of emerging technologies, such as natural language processing and graph computing, highlights the potential for more context-aware, interpretable, and scalable recommendation frameworks. By leveraging these approaches, intelligent recommendation systems can provide more diversified, user-centric services, thereby promoting higher engagement and supporting the sustainable development of e-commerce ecosystems.