Multi-objective off-policy learning for page composition

Abstract

A system may receive user information associated with a user, third-party information associated with a third party, and a relevance-ordered list of media carousels comprising a plurality of media carousels. A system may collect historical log information comprising a plurality of displayed pages comprising media carousels and assign a reward value to each of the displayed pages to generate a reward vector, the reward value based on a user interaction associated with each displayed page. A system may estimate a logging policy based in part on the historical log information, and a target policy based in part on the reward vector. A system may train a carousel selection model by the target policy and the logging policy, then use the carousel selection model to generate a result. A system may select a plurality of media carousels from the relevance-ordered list.