algo-rec-hybridlisted

# Hybrid Recommendation System ## Overview Hybrid recommendation combines multiple strategies (CF, content-based, knowledge-based) to overcome individual method limitations. Common architectures: weighted, switching, cascade, feature augmentation, and meta-level. Complexity varies by architecture. ## When to Use **Trigger conditions:** - Building a production recommendation system that must handle cold start AND personalization - Single methods have known weaknesses for your use case - Need to balance accuracy, diversity, and coverage **When NOT to use:** - When you have a single clean data source (start with the matching single method first) - When system simplicity is more important than marginal accuracy gains ## Algorithm ``` IRON LAW: Hybrid Adds Value ONLY With Complementary Strengths Combining two systems with the SAME weakness amplifies the weakness. CF fails on cold start + content-based fails on cold start = hybrid STILL fails on cold start. Choose components that cover each other's gaps. ``` ### Phase 1: Input Validation Identify available data: interaction history (for CF), item features (for content-based), contextual signals (time, device, location). Map data to method capabilities. **Gate:** At least two complementary data sources available. ### Phase 2: Core Algorithm **Weighted hybrid:** Score = α × CF_score + β × CB_score. Tune weights via cross-validation. **Switching hybrid:** Use CF when sufficient data exists; switch to content-based for cold s