User Profiling: Concepts, Stages, and Data Modeling Methods

User profiling (User Profile) is presented as a foundational technique in big data that abstracts a complete view of a user by labeling demographic, behavioral, and preference information, enabling precise and rapid analysis of user habits and consumption patterns.

The article describes the four stages of building a user profile: data collection, tag definition, hierarchical classification, and weight assignment. Tags are concise, human‑readable attributes such as age, gender, location, or preferences, and combining all tags yields a multidimensional portrait of each user.

It emphasizes the importance of profiling for tasks like classification statistics, data mining, and improving algorithmic services such as search, recommendation, and advertising. Weighting mechanisms consider time decay, behavior type, and URL relevance, allowing the model to reflect the varying influence of different actions.

Two main data sources are identified: static information (stable demographic and commercial attributes) and dynamic information (continuous online behaviors). The article details how to model events using user identifier, timestamp, behavior type, and touch‑point (URL + content), and provides formulas for calculating tag weights.

Practical examples illustrate how a browsing event on a wine e‑commerce site translates into weighted tags (e.g., "red wine" 0.665). The discussion extends to other domains such as video streaming, gaming, and financial services, showing how cross‑screen data and external sources enrich profiles.

Finally, the article notes that while specific algorithms are not covered, the presented framework offers a systematic, scalable approach to constructing user profiles that can be adapted to various industries and product types.