User Behavior Segmentation & Predictive Profiling
Behavior modeling pipeline for recommender systems using clustering, anomaly detection, sequence classification, and drift monitoring
Overview
Built a user behavior segmentation and predictive profiling pipeline for personalization and recommender systems. The project focuses on modeling temporal behavioral patterns while keeping deployment trade-offs visible.
Key Work
- Applied supervised and unsupervised learning to behavioral event streams, including clustering, anomaly detection, and sequence classification.
- Benchmarked XGBoost, transformer, and LSTM architectures across accuracy and latency trade-offs.
- Designed temporal feature engineering workflows for behavioral preference modeling.
- Added drift-detection harnesses to monitor changing behavior distributions over time.
Stack
- Python, scikit-learn, PyTorch, XGBoost
- Hugging Face Transformers, temporal feature engineering
- Model evaluation, drift detection, recommender-system profiling
Impact
The pipeline supports user understanding workflows for ranking and recommendation systems, with practical emphasis on measurable quality, serving cost, and model monitoring.