Claude
|
aab54ea7c0
|
docs: Add comprehensive multi-task learning architecture and gameplan
Created detailed documentation for implementing multi-task learning system
to improve agent detection and dynamic pricing:
- GAMEPLAN_MULTITASK_PRICING.md: Complete 50+ page technical specification
including feature engineering, supervised learning, multi-task neural
networks, synthetic simulator, and knowledge distillation approach
- ARCHITECTURE_OVERVIEW.md: Quick reference with visual diagrams comparing
current rule-based system to proposed ML architecture, metrics, and
implementation phases
Key improvements proposed:
- Replace O(n²) SessionState pipeline with vectorized feature extraction
- Train XGBoost classifier on experimentId labels (ROC-AUC >0.90 target)
- Multi-task neural network for joint agent detection + purchase prediction
- Gymnasium-based synthetic pricing environment for safe experimentation
- Knowledge distillation to extract interpretable pricing heuristics
Addresses margin leakage concerns with learned pricing strategies instead
of simple velocity thresholds.
|
2025-12-11 09:51:41 +00:00 |
|