-------------------------------------| 1. DATA SOURCES |
| User interaction logs, clickstream|
| histories, and product catalog |
| exports form core inputs. |
| Additional datasets such as |
| RetailRocket samples from Kaggle |
| provide diversity. Data validated |
| for freshness and sparsity. |
------------------------------------- | v ----------------------------------- | 2. INGESTION & PREPROCESSING |
| User-event tables are deduped, |
| sessionized, and time-aligned |
| to capture evolving preferences.|
| Missing attributes imputed. |
| Normalization ensures stable |
| ranges. |
----------------------------------- | v -------------------------------- | 3. FEATURE ENGINEERING |
| User and item embeddings |
| learned using matrix factor. |
| Behavioral similarity and |
| co-occurrence strengthen |
| personalization. |
-------------------------------- | v ----------------------------------- | 4. MODEL TRAINING (Hybrid MF + |
| NCF) |
| Neural Collaborative Filtering |
| and MF models trained jointly. |
| Cross-validation stabilizes. |
| Ranking objectives enhance |
| top-N relevance. |
----------------------------------- | v ----------------------------------- | 5. INFERENCE & SERVING |
| Real-time recommendation API |
| serves ranked lists with low |
| latency. FAISS accelerates |
| similarity search. Responses |
| adapt dynamically. |
----------------------------------- | v------------------------------------| 6. MONITORING & FEEDBACK LOOP |
| User clicks, purchases, dwell |
| times close learning loop. |
| Performance metrics track drift. |
| A/B tests compare strategies. |
| Continuous retraining ensures |
| evolution with trends. |
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