What is interleaving in search and recommendations?

Interleaving in search and recommendations is a method of mixing results from different sources, models, or options into a single list to improve content quality and diversity.

For example, if there are several ranking algorithms or different types of content (news, products, videos), interleaving allows combining their results into a single stream rather than displaying them separately or in blocks.

This is useful for:

• Conducting A/B tests, where different variants are mixed in one list, reducing the influence of user preferences and increasing statistical significance.
• Increasing the diversity of results so that users see a broader range of relevant content.

Example: in search results, results from two different models can be alternated — the first position from model A, the second from model B, the third again from A, and so on. This allows evaluating which model performs better in real-world conditions.