Hybrid recommendation systems handle data sparsity by integrating multiple recommendation techniques, such as collaborative filtering and content-based filtering. This combination allows the system to exploit the strengths of each method and provide more accurate recommendations even when there is limited interaction data available. For example, content-based filtering can help fill in the gaps when collaborative filtering struggles due to sparse data.