The Ranking Context That History Forgot
TL;DR for operators Recommendation platforms already compute detailed information about users, items, and competing candidates when making ranking decisions. Much of that contextual information disappears when an encounter becomes another item in a user’s historical sequence. PRISM1 proposes retaining a compressed, personalized representation of each historical scoring encounter. On Meta’s Surface 1, the resulting history features reduce average offline normalized entropy by 0.15%, compared with 0.07% for VISTA, an alternative that caches user-history summaries. ...