US 2020/0005354 A1Published application

Machine learning techniques for multi-objective content item selection

AssigneeMicrosoft Technology Licensing, LLC

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

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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.

Claims14

Dependent claims

A method comprising:

storing resource allocation data that indicates, for each campaign of a plurality of campaigns, a resource allocation amount that is assigned by a central authority;
storing targeting criteria data that indicates, for each campaign of the plurality of campaigns, one or more targeting criteria that is assigned by a different entity of a plurality of entities, each of which is different than the central authority;
receiving a content request;
in response to receiving the content request:
identifying a subset of the plurality of campaigns based on the targeting criteria data;
generating a plurality of scores for the campaigns in the subset, wherein each score in the plurality of scores reflects a likelihood that a content item of the corresponding campaign will be selected;
based on a plurality of scores, selecting a particular campaign from the subset;
causing a particular content item of the particular campaign to be displayed;
identifying a particular resource allocation amount that is associated with the particular campaign;
determining a resource reduction amount associated with displaying the particular content item of the particular campaign;
reducing the particular resource allocation based on the resource reduction amount;
wherein the method is performed by one or more computing devices.

9 more elements

A method comprising:

storing value data that indicates, for each campaign of a plurality of campaigns, a value that is assigned to said each campaign;
storing targeting criteria data that indicates, for each campaign of the plurality of campaigns, one or more targeting criteria;
applying a multi-objective optimization technique to calculate a first weight for value and a second weight for cost;
calculating a plurality of costs, wherein each cost of the plurality of costs indicates, for each campaign of the plurality of campaigns, a cost of presenting a content item of said each campaign;
receiving a content request;
in response to receiving the content request:
identifying a subset of the plurality of campaigns based on the targeting criteria data;
generating a plurality of scores for the campaigns in the subset, wherein each score in the plurality of scores corresponds to a different campaign in the subset and is based on the value data, a cost of the plurality of costs, the first weight, and the second weight;
based on a plurality of scores, selecting a particular campaign from the subset;
causing a particular content item of the particular campaign to be displayed;
wherein the method is performed by one or more computing devices.

8 more elements

A method comprising:

storing targeting criteria data that indicates, for each campaign of the plurality of campaigns, one or more targeting criteria;
wherein each campaign corresponds to an objective of a plurality of objectives;
applying a multi-objective optimization technique to calculate a plurality of weights, wherein each weight of the plurality of weights corresponds to a different objective of the plurality of objectives;
calculating a plurality of costs, wherein each cost of the plurality of costs indicates, for each campaign of the plurality of campaigns, a cost of presenting a content item of said each campaign;
receiving a content request;
in response to receiving the content request:
identifying a subset of the plurality of campaigns based on the targeting criteria data;
generating a plurality of scores for the campaigns in the subset, wherein each score in the plurality of scores corresponds to a different campaign in the subset and is based on a subset of the plurality of weights and a cost of the plurality of costs;
based on a plurality of scores, selecting a particular campaign from the subset;
causing a particular content item of the particular campaign to be displayed;
wherein the method is performed by one or more computing devices.

8 more elements

Dependencies are inferred from the claim text.