What is data sparsity in recommendation systems?

What is data sparsity in recommendation systems?

Data sparsity refers to the lack of sufficient user-item interaction data in recommendation systems, making it hard to generate accurate recommendations.

Why is data sparsity a problem for recommendations?

It leads to less reliable predictions, as the system has fewer data points to understand user preferences and item similarities.

How can we mitigate data sparsity?

Techniques like collaborative filtering, content-based filtering, and hybrid methods can help alleviate data sparsity issues.

Can you give an example of a hybrid method?

Sure! A hybrid method combines collaborative filtering and content-based filtering, using both user behavior and item attributes to improve recommendations.