"algo-rec-hybrid"

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 start items/users.

Cascade hybrid: First stage filters (e.g., content-based), second stage ranks (e.g., CF) within filtered set.

Feature augmentation: Use one method's output as input features for another (e.g., CF embeddings as content features).

Phase 3: Verification

A/B test hybrid vs individual components. Measure: accuracy (NDCG, precision@K), coverage (% of catalog recommended), diversity (intra-list diversity). Gate: Hybrid outperforms best individual component on primary metric.