User Profiling: Concepts, Tag Classification, Tag‑System Construction, Applications and Implementation Steps

User profiling (also called user‑information tagging) abstracts a user's attributes, preferences, habits and behaviors into a set of concise tags that can be easily understood by humans and processed by computers.

The profiling framework consists of five aspects: Goal – describing, understanding and recognizing users; Method – informal (text, images, video) and formal (data‑driven) approaches; Organization – structured or unstructured data; Standards – common‑sense and knowledge‑based refinement; Validation – factual, verifiable sources.

Tags can be classified in multiple ways: static vs. dynamic, qualitative vs. quantitative, source‑based (basic, business, intelligent), hierarchical (level‑1, level‑2, …), and by data processing dimension (fact, model, prediction). Fact tags are directly extracted from raw data, model tags are derived through clustering or classification, and prediction tags are generated by machine‑learning or statistical models.

The tag‑system is built by defining a taxonomy (leaf tags and parent tags), assigning attributes (inherent, derived, behavioral, attitude, test), and establishing naming and value rules. Tag attributes help trace the origin of each label and its meaning.

Typical modeling layers include: Raw Input (user history, membership, transaction logs); Fact Layer (demographic, behavioral, consumption attributes); Model Layer (regression, decision trees, SVM, clustering, TF‑IDF, LDA, topic models); Prediction Layer (supervised learning, econometric regression, linear programming) that feed into marketing models.

Key algorithms mentioned are TF‑IDF, LDA, clustering, classification, regression, decision trees, and support‑vector machines, which are used to extract fact tags, build model tags and generate prediction tags.

Application scenarios span precise marketing, user statistics, data‑driven recommendation/search/advertising systems, product‑service improvement, industry reports, and user research. Benefits include better user understanding, higher conversion, reduced churn, and increased profitability.

The overall architecture follows a layered design: data source layer (core systems, CRM, internet platforms, third‑party data), data collection layer (structured, semi‑structured, unstructured, multimedia), data modeling layer (cleaning, ID unification, analysis, profiling), data application layer (analysis, service, marketing, APIs) and industry‑specific layers.

Implementation steps are: 1) Data collection (online, offline, third‑party); 2) Data cleaning; 3) Data standardization (min‑max, Z‑score, decimal scaling); 4) Data modeling (event modeling, weight calculation using behavior type weight, time decay, frequency, TF‑IDF); 5) Tag mining (rule‑based or topic‑model based); 6) Data visualization (charts, tables, dashboards) to support decision‑making.