Sub-Population Feature Identification for Content Personalization

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Solution Overview

Problem

Existing recommendation systems struggle to personalize content effectively for sub-populations within a gross population, often relying on subjective methods and extensive A/B testing, which can be resource-intensive and inefficient.

Innovation Solution

A system that identifies sub-populations by comparing feedback metrics from content presented using incumbent and alternative statistical models, associating sub-population features with the most statistically significant differences, and using this information to personalize content delivery.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If subjective methods and extensive A/B testing are used for content personalization, then content relevance may be improved, but computational resources and time consumption increase significantly

Engineering Contradiction:

Improvecontent relevanceVSAvoidtime consumption

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The patent segments the gross population into distinct sub-populations based on shared characteristics and feedback patterns. By dividing the population into smaller, more homogeneous groups, the system can apply personalized content strategies to each sub-population independently, improving content relevance without requiring exhaustive A/B testing across the entire population. This segmentation reduces the computational burden while maintaining or enhancing personalization effectiveness.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The system performs preliminary analysis to identify sub-populations and their characteristic feedback patterns before conducting content delivery. By pre-segmenting the population and establishing sub-population features in advance, the system avoids the need for extensive real-time A/B testing, thereby reducing time consumption while still achieving accurate content personalization when needed.

Inventive Principle:

Principle #10Preliminary action

2Measurement precision

If subjective methods and extensive A/B testing are used for content personalization, then content relevance may be improved, but computational resources increase significantly

Engineering Contradiction:

Improvecontent relevanceVSAvoidcomputational resources

Core Design Contradiction:

Measurement precisionVSUse of energy by moving object

Solution Approach 1:

By segmenting the population into sub-populations with shared characteristics, the system reduces the computational scope required for personalization. Instead of analyzing and testing against the entire gross population, the system focuses computational resources on smaller, more homogeneous sub-groups, thereby reducing overall computational resource consumption while maintaining content relevance through targeted analysis.

Inventive Principle:

Principle #1Segmentation

Solution Approach 2:

The patent extracts and utilizes specific sub-population features that are most relevant for content personalization. By identifying and focusing on key distinguishing characteristics of sub-populations rather than analyzing all possible variables, the system reduces computational complexity and resource requirements while still achieving effective content personalization.

Inventive Principle:

Principle #2Taking out (Extraction)

3Productivity

If sub-population features are identified through statistical analysis of feedback metrics, then content personalization efficiency is improved, but system complexity increases

Engineering Contradiction:

Improvepersonalization efficiencyVSAvoidsystem complexity

Core Design Contradiction:

ProductivityVSDevice complexity

Solution Approach 1:

The system employs feedback mechanisms by analyzing user responses to content delivery and using this feedback to identify sub-population features. This feedback-driven approach allows the system to automatically learn and adapt to population characteristics without requiring complex manual configuration, thereby improving personalization efficiency while keeping the system architecture relatively simple and self-adjusting.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS11860880B2Systems for learning and using one or more sub-population features associated with individuals of one or more sub-populations of a gross population and related methods therefor

Publication Date: 2024.01.02 WALMART APOLLO LLC

AI summary

A system including one or more processors and one or more non-transitory media storing computer instructions configured to run on the one or more processors and perform: identifying a first sub-population of case individuals from a gross population of the case individuals; presenting first test content to a first test sub-population of the case individuals, the first test content is selected according to a first statistical model; measuring a first test sub-population average feedback metric based on first test content feedback provided from the first test sub-population of the case individuals; determining that the first test sub-population average feedback metric exceeds a first control population average feedback metric of a first control population of the case individuals; and determining that a probability value for a difference between the first test sub-population average feedback metric and the first control population average feedback metric is less than a predetermined significance level value. Other embodiments are disclosed.