Overcoming Challenges in User Profiling: A Big Data‑Driven Framework for Precise Marketing

User profiling transforms raw consumer data—such as demographics, behaviors, and preferences—into comprehensive, tag‑based representations, serving as a fundamental application of big‑data technology for businesses.

The traditional approach suffers from isolated data sources, limited cross‑domain insight, and a lack of functional breakthroughs; the presented profiling solution tackles these issues through a multi‑step framework.

First, it breaks data silos by unifying underlying datasets, standardizing storage and computation while still supporting personalized scenarios; this shared‑tag architecture creates a renewable data resource with synergistic value.

Second, by consolidating data from multiple PC‑based business lines, redundant tag construction is avoided, reducing development costs and boosting computational efficiency; user tags become business‑oriented, intuitive, and reusable.

Third, the platform abstracts functionality to decouple from downstream marketing and operation systems, allowing rule and algorithm integration that powers precise marketing across four modules: person‑to‑person, item‑to‑person, item‑to‑item, and person‑to‑item.

Fourth, the tag‑system design follows principles of data‑driven abstraction, employing text‑mining algorithms such as TF‑IDF, Topic Modeling, LDA, and PU‑learning, with Spark and RHadoop for processing, while MySQL stores user states and JWT manages permissions.

The final platform visualizes tag outputs via components, builds tags through a “micro‑division” method, and supports tag marketplaces, user segmentation, and insight pages; it also merges custom tags from various business lines into a unified tag ecosystem.

Looking ahead, the profiling system (now in its 2.5 version) adds intelligent clustering to enable a closed‑loop marketing platform that selects content‑preferring audiences, configures targeted delivery, visualizes results, and iterates based on BI feedback, thereby enhancing both accuracy and business metrics.