公开(公告)号：US20180189673A1

公开(公告)日：2018-07-05

申请号：US15394875

申请日：2016-12-30

Applicant: Google Inc.

Inventor： Jeffrey Dalton ,  Karthik Raman ,  Tobias Schnabel ,  Evgeniy Gabrilovich

IPC: G06N99/00 ,  G06N3/08 ,  G06F17/30

CPC classification number: G06N20/00 ,  G06F16/248 ,  G06F16/951 ,  G06F16/9535 ,  G06N3/08

Abstract: The present disclosure provides systems and methods that use machine learning to improve whole-structure relevance of hierarchical informational displays. In particular, the present disclosure provides systems and methods that employ a supervised, discriminative machine learning approach to jointly optimize the ranking of items and their display attributes. One example system includes a machine-learned display selection model that has been trained to jointly select a plurality of items and one or more attributes for each item for inclusion in an informational display. For example, the machine-learned display selection model can optimize a nested submodular objective function to jointly select the items and attributes.