Research on E-commerce User Profile Construction and Personalized Service Strategies Based on Multi-Source Heterogeneous Data

As e-commerce enters an era of intensive competition, data-driven refined operation has become essential for improving service accuracy and user conversion. However, user data are often scattered across different platforms and systems, producing data silos, distorted user profiles, and ineffective recommendations. This study proposes a user profile construction framework based on multi-source heterogeneous data fusion. E-commerce transaction data, behavioral logs, social media content, search records, product information, and customer service records are integrated through data cleaning, entity alignment, and feature fusion. A multidimensional tag system is then constructed from five dimensions: basic attributes, consumption power, interest preference, social relationship, and lifecycle stage. Based on the resulting profiles, dynamic personalized service strategies are designed through user segmentation and recommendation mechanisms, and their effectiveness is verified through A/B testing. The results show that user profiles constructed from multi-source data achieve significantly higher coverage and accuracy than profiles based on a single data source. The study provides an operable framework for overcoming data barriers and upgrading intelligent e-commerce services.