公开(公告)号：US20200005354A1

公开(公告)日：2020-01-02

申请号：US16024753

申请日：2018-06-30

Applicant: Microsoft Technology Licensing, LLC

Inventor： Rupesh Gupta ,  Guangde Chen ,  Curtis Chung-Yen Wang ,  Deepak K. Agarwal ,  Souvik Ghosh ,  Shipeng Yu

IPC: G06Q30/02 ,  G06Q10/06 ,  G06N99/00

Abstract: Machine learning techniques for multi-objective content item selection are provided. In one technique, resource allocation data is stored that indicates, for each campaign of multiple campaigns, a resource allocation amount that is assigned by a central authority. In response to receiving the content request, a subset of the campaigns is identified based on targeting criteria. Multiple scores are generated, each score reflecting a likelihood that a content item of the corresponding campaign will be selected. Based on the scores, a particular campaign from the subset is selected and the corresponding content item transmitted over a computer network to be displayed on a computing device. A resource allocation amount that is associated with the particular campaign is identified. A resource reduction amount associated with displaying the content item of the particular campaign is determined. The particular resource allocation is reduced based on the resource reduction amount.