US 12,639,728 B1Granted

Machine learning techniques for content delivery service

AssigneeAmazon Technologies, Inc.

InventorsIoannis Ioannidis, Anton Dmitriyevich Postnov

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Abstract

A system is disclosed to host a first machine learning model and a second machine learning model, where the first machine learning model is trained using a first privacy-compliant dataset, and the second machine learning model is trained using a second privacy-compliant dataset. The system is to obtain a digital content request that includes a dataset including user data and digital content data. The dataset is processed using the first machine learning model to generate a first score representing a probability of a user engaging with digital content and using the second machine learning model to generate a second score representing a similar probability. Based on one or both of these scores, the system selects at least one digital content from a plurality of digital content.

Claims20

Dependent claims

A computer-implemented method, comprising:

providing a data clean room to host first data from an external entity and second data of an entity that provides the data clean room, the data clean room configured to anonymize the first and second data to generate first anonymized data and second anonymized data, the data clean room further configured to at least restrict reading the first data and the second data based on one or more predefined permissions associated with the data clean room;
providing a service to host at least a first trained machine learning (ML) model and a second trained ML model, the first ML model trained using user data, digital content data, and contextual data determined by the external entity and at least partially comprised in the first anonymized data and the second trained ML model trained using user data, digital content data, and contextual data determined by the entity and at least partially comprised in the second anonymized data;
obtaining a digital content request triggered based on a digital content opportunity on a computing device, the digital content request comprising an information dataset including information associated with the computing device, anonymized user information, and contextual information;
processing the information dataset using the first trained ML model to generate a first propensity score representing a first probability of a user engaging digital content;
processing the information dataset using the second trained ML model to generate a second propensity score representing a second probability of a user engaging digital content;
and
based, at least in part, on one or more of the first and second propensity scores, selecting at least one digital content from a plurality of digital content to display on the computing device.

4 more elements

A system, comprising:

one or more processors;
memory that stores computer-executable instructions that are executable by the one or more processors to cause the system to:
cause, via access to a data clean room, generation of a first privacy-compliant dataset associated with a first entity and a second privacy-compliant dataset associated with a second entity based at least in part on first data associated with the first entity and second data associated with the second entity, wherein the data clean room includes one or more permissions that restrict whether at least one of the first entity or the second entity may perform one or more operations using at least a portion of the first data or at least a portion of the second data via the data clean room;
host a first machine learning (ML) model and a second ML model, the first ML model trained using the first privacy-compliant dataset and the second ML model trained using the second privacy-compliant dataset, the first privacy-compliant dataset associated with a first entity that is distinct from a second entity associated with the second privacy compliant dataset or the first ML model comprises embedded proprietary parameters caused to be obfuscated by the first entity to prevent the second entity from accessing the proprietary parameters;
obtain a digital content request including a dataset comprising user data and digital content data;
process the dataset using the first ML model to generate a first score representing a first probability of a user engaging digital content;
process the dataset using the second ML model to generate a second score representing a second probability of a user engaging digital content;
and
select at least one digital content from a plurality of digital content based on one or more of the first and second scores.

6 more elements

A non-transitory computer-readable storage medium storing thereon executable instructions that, as a result of being executed by one or more processors of a computer system, cause the computer system to:

cause, via access to a data clean room, generation of a first privacy-compliant dataset associated with a first entity and a second privacy-compliant dataset associated with a second entity based at least in part on first data associated with the first entity and second data associated with the second entity, wherein the data clean room includes one or more permissions that restrict whether at least one of the first entity or the second entity may perform one or more operations using at least a portion of the first data or at least a portion of the second data via the data clean room;
host a first machine learning (ML) model and a second ML model, the first ML model trained using the first privacy-compliant dataset and the second ML model trained using the second privacy-compliant dataset, the first privacy-compliant dataset associated with a first entity that is distinct from a second entity associated with the second privacy-compliant dataset or the first ML model comprises embedded proprietary parameters caused to be obfuscated by the first entity to prevent the second entity from accessing the proprietary parameters;
obtain a digital content request including a dataset comprising user data and digital content data;
process the dataset using the first ML model to generate a first score representing a first probability of a user engaging digital content;
process the dataset using the second ML model to generate a second score representing a second probability of a user engaging digital content;
and
select at least one digital content from a plurality of digital content based on one or more of the first and second scores.

4 more elements

Dependencies are inferred from the claim text.