User Profiling Methodology and Engineering Solutions

Introduction: In the era of big data, user behavior can be traced and analyzed, making user profiling essential for precise marketing and refined operations.

Tag Types: User tags are categorized into statistical tags, rule‑based tags, and machine learning tags, each with distinct generation methods and usage.

Data Architecture: The profiling system relies on infrastructure such as Spark, Hive, HBase, Airflow, MySQL, Redis, and Elasticsearch, with a data warehouse architecture that includes ODS, DW, and DM layers and supports ETL processes.

Modules: The solution covers eight modules including profiling basics, metric system, tag storage, tag development, ETL scheduling, service‑layer integration, productization, and application promotion.

Development Process: Seven stages—from goal definition, task decomposition, scenario discussion, data scope confirmation, feature selection, offline testing, to online deployment—outline the workflow and key deliverables.

Case Study: A book e‑commerce platform example demonstrates how user, order, log, and other tables are used to build profiles, with examples of HiveQL and Python/Scala code for tag generation and table design.

Table Design: Both daily full‑snapshot and daily incremental tables are described, including partitioning strategies and sample insert and query statements. insert overwrite table dw.userprofile_userlabel_all partition(data_date='20190101', theme='member', labelid='ATTRITUBE_U_05_001') select count(distinct userid) from dw.userprofile_userlabel_all where data_date='20190101' select * from dw.userprofile_act_feature_append where userid='001' and data_date>='20180701' and data_date<='20180707'

Qualitative Profiling: In addition to quantitative methods, qualitative surveys and questionnaires are discussed as complementary approaches.

Conclusion: The article provides a comprehensive overview of user profiling concepts, architecture, development phases, and practical implementation guidance.