Dynamic evaluation and use of global and contextual personas

Patent metadata

Field	Value
Publication number	US-11790399-B2
Application number	US-202217747657-A
Country	US
Kind code	B2
Filing date	May 18, 2022
Priority date	Jan 21, 2020
Publication date	Oct 17, 2023
Grant date	Oct 17, 2023

Abstract

A system and method for content selection and presentation is disclosed. A plurality of content elements configured for presentation in at least one content container is received and one of the plurality of content elements is selected for presentation in the at least one content container. The one of the plurality of content elements is selected by a trained selection model configured to select one of an individual context or a global context. An interface including the selected one of the plurality of content elements is generated.

First claim

What is claimed is:

1. A system comprising:
a memory resource storing instructions; and
a processor coupled to the memory resource, the processor being configured to execute the instructions to:
for a user, receive training data including impression data indicating one or more instances the user interacted with one or more content elements on a user interface presented by a device of the user, the user interface including a plurality of containers, and each of the one or more instances being associated with one of a plurality of individual contexts;
obtain, from a database, a plurality of content elements configured for presentation in at least a first content container of the plurality of containers;
iteratively implement a machine learning process that generates a trained selection model by utilizing a reinforcement learning mechanism and at least an individual explore-exploit mechanism;
based at least on the training data, implement, by utilizing the trained selection model, a context selection process that selects a context to be assigned to the user, the context selection process comprising:
based at least on a first subset of training data, determining an expected future reward value of each of one or more individual contexts of the training data;
based at least on the training data, determining an expected future reward value of a global context, wherein the expected future reward value of the global context (CTR global ) is determined according to:

CTR
global

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k
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1

n

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q
k

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p
k

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where p k is a past click-through rate, p j is a reward value for a j-th interaction, and q k is a future click-through rate;
based on a comparison of the expected future reward value of each of the one or more individual contexts of the training data and the expected future reward value of the global context, select the global context or one of the one or more individual contexts.

2. The system of claim 1 , wherein the processor executes the instructions further to:
select and present one of the plurality of content elements in the first content container of the user interface on a display of the device of the user based on the selected global context or one of the one or more individual contexts.

3. The system of claim 1 , wherein the expected future reward value is associated with a click-through rate.

4. The system of claim 1 , wherein the expected future reward value is determined using Thompson sampling.

5. The system of claim 1 , wherein the impression data includes data characterizing a click-through rate, data characterizing a user return rate, or data characterizing historic purchase data.

6. The system of claim 1 , wherein the trained selection model is trained by combining two or more individual contexts into a single individual context.

7. The system of claim 6 , wherein the two or more individual contexts are grouped based on a pairwise distance.

8. A system, comprising:
a memory resource storing instructions; and
a processor coupled to the memory resource, the processor being configured to execute the instructions to:
for a user, receive training data including impression data indicating one or more instances the user interacted with one or more content elements on a user interface presented by a device of the user, the user interface including a plurality of containers, and each of the one or more instances being associated with one of a plurality of individual contexts;
obtain, from a database, a plurality of content elements configured for presentation in at least a first content container of the plurality of containers;
iteratively implement a machine learning process that generates a trained selection model by utilizing a reinforcement learning mechanism and at least an individual explore-exploit mechanism;
based at least on the training data, implement, by utilizing the trained selection model, a context selection process that selects a context to be assigned to the user, the context selection process comprising:
based at least on a first subset of training data, determining an expected future reward value of each of one or more individual contexts of the training data;
based at least on the training data, determining an expected future reward value of a global context, wherein the expected future value of at least one of the one or more contexts of the training data (CTR contextual ) is determined according to:

CTR
contextual

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Assignees

• Walmart Apollo Llc

Inventors

• Deng Yang
• Deshpande Omkar
• Ali Afroza
• Mitra Abhimanyu
• Achan Kannan

Classifications

• G06Q30/0255 Primary

  based on user history · CPC title

• G06Q30/0246

  Traffic · CPC title

• G06Q30/0268

  at point-of-sale [POS] · CPC title