Development and Construction of Product Selection System for Cross-Border E-commerce Platform under Big Data Technology

Difficulty in product selection is a common issue faced by all cross-border e-commerce users. This research aims to address this pain point through the application of big data technology. We construct an intelligent product selection and recommendation system that efficiently processes vast amounts of product information and achieves personalized precision recommendations. The key innovation of this system lies in utilizing vector similarity algorithms to match based on product features and user preference models, thereby generating personalized recommendations for each user. We employ SOA architecture and Docker containers for system design and implementation, and validate the system's superiority through multidimensional testing. This study demonstrates the significant role of big data technology in facilitating refined product recommendations in the context of cross-border e-commerce, effectively enhancing user experience. It provides valuable insights for the development of intelligent product selection systems on other e-commerce platforms.