What are the main challenges of hybrid recommendation systems?

What are the main challenges of hybrid recommendation systems?

Hybrid recommendation systems combine multiple recommendation techniques, but they face challenges like data sparsity, scalability, and integration of different models.

Can you explain data sparsity in more detail?

Data sparsity occurs when there is insufficient data to make accurate recommendations, leading to poor performance of the system.

How do you address scalability issues?

Scalability can be addressed by using efficient algorithms, distributed computing, and optimizing data storage to handle large datasets.

What about the integration of different models?

Integrating different models requires careful design to ensure they complement each other, which can be complex and resource-intensive.