In the preceding chapters, we constructed recommendation systems using content-based filtering and collaborative filtering. While effective in certain scenarios, each approach has inherent limitations. Content-based systems struggle to provide recommendations for new users, and collaborative filters are challenged by the cold-start problem for new items and the effects of data sparsity.

This chapter introduces hybrid recommendation systems, a practical method for mitigating these weaknesses by combining the strengths of different algorithms. By blending multiple models, we can often build a more resilient and accurate system that performs well across a wider range of conditions.

You will learn several common techniques for combining models, including:

• Weighted Hybridization: Combining the prediction scores from different recommenders using a simple linear formula, such as $Score_{hybrid} = \alpha \cdot Score_{content} + (1-\alpha) \cdot Score_{collab}$.
• Switching and Mixed Techniques: Dynamically selecting a model or blending ranked lists based on specific criteria, such as data availability for a given user or item.
• Feature Combination: Creating a single, more sophisticated model by using the outputs of one recommender as input features for another.

The chapter concludes with a hands-on implementation where you will construct a weighted hybrid recommender, putting these methods into practice to build a system that leverages both content and collaborative signals.