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