Learning-to-Rank with Nested Feedback

Understand

Many platforms on the web present ranked lists of content to users, typically optimized for engagement-, satisfaction- or retention- driven metrics.

• Advances in the Learning-to-Rank (LTR) research literature have enabled rapid growth in this application area.
• Several popular interfaces now include nested lists, where users can enter a 2nd-level feed via any given 1st-level item.
• Naturally, this has implications for evaluation metrics, objective functions, and the ranking policies we wish to learn.