Sequence Pattern Mining for User Behavior Analysis in Xianyu
By applying sequence pattern mining and unsupervised clustering to Xianyu’s massive event logs, the study abstracts high‑level user behaviors, discovers frequent subsequences, uncovers unknown fraudulent account patterns, expands known fraud cohorts with 99 % precision, and enables richer analyses such as PCA‑based cross‑group comparisons.
Background: User behavior analysis extracts actionable insights from product event data to guide design and operations.
Typical pipeline: data collection → cleaning → presentation. Xianyu’s massive raw event logs suffer from low utilization and low‑level granularity.
We abstract higher‑level behaviors using sequence pattern mining, which finds frequent subsequences in large event sequences.
Example pattern: event2 → event4 → event7 appears across many users, indicating a common behavior.
Combining unsupervised clustering with sequence mining reveals characteristic patterns of user groups.
Case 1 – Detecting unknown fraudulent accounts: a cluster showed the pattern “search results → open product → chat → send file → back to results…”, all files were ad videos, uncovering a new black‑industry cohort.
Case 2 – Expanding a known fraud cohort: using the pattern “search → click → chat → send message → view profile → follow” we identified 57 % more accounts with 99 % precision.
The mined behavior sequences also provide a “user‑behavior count” view for further analysis such as PCA filtering and cross‑group comparisons, illustrating the broader potential of sequence pattern mining.
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