  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. We propose a theoretically grounded method
to incorporate 2nd-level feedback into any 1st-level ranking model. Online
experiments on a large-scale recommendation system confirm our theoretical
findings.
