Research on E-Commerce Product Recommendation Algorithms Integrating Knowledge Graphs Under Data Compliance and Cross-Border Data Flows

Abstract

With the rapid growth of e-commerce platforms and user behavioral data, product recommendation systems play an increasingly important role in improving user experience, click-through rates, and long-term commercial value. However, traditional recommendation methods often suffer from data sparsity, cold-start problems, limited interpretability, and insufficient modeling of semantic relationships among users and products. To address these challenges, this study proposes an e-commerce product recommendation framework integrating knowledge graphs, with the Taobao recommendation system as the main application scenario. Overall, this study provides an effective and scalable solution for intelligent e-commerce product recommendation systems.