A User Profile System for the Finance Platform of Commerce

With the rapid advancement of big data technology, user profiling has emerged as a critical tool for enterprises to refine operations and enhance precision marketing. This paper explores the application of user profiling within the financial platform of commerce, focusing on the integration of internal and external data resources, data mining techniques, and the construction of a user profile model and system. The proposed system aims to achieve precise marketing, risk control, and informed decision-making by providing comprehensive insights into user behavior and preferences. The system encompasses modules for individual and group user profiles, behavior trend analysis, and tag management, facilitating detailed user segmentation and targeted marketing strategies. A sample K-means clustering algorithm is also presented to demonstrate user grouping based on shared characteristics. The findings highlight the potential of user profiling to improve customer satisfaction and overall competitiveness in the financial industry, while promoting cross-industry data integration and the continuous evolution of user profile models.