Un-biasing User Personalizations in Recommendation Engines

Resolve Bottlenecks,
Find Innovative SolutionsGenerate Solutions

Solution Overview

Problem

Existing content delivery systems often personalize content for users based on their interests and behaviors, leading to a limited range of content being presented, which can result in users being 'pigeon-holed' and missing out on diverse content options.

Innovation Solution

The proposed system provides a framework that allows users to customize their personalization settings, using a recommendation engine that analyzes user requests and modeled behaviors to offer a broader range of content from a larger pool, thereby breaking free from preconceived content recommendations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If content delivery systems personalize content based on user interests and behaviors, then content relevance to user is improved, but content diversity presented to user deteriorates

Engineering Contradiction:

Improvecontent relevanceVSAvoidcontent diversity

Core Design Contradiction:

Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The system dynamically adjusts personalization intensity based on user interactions and context, allowing the recommendation algorithm to flex between highly personalized content and diverse content exploration, thereby resolving the contradiction between relevance and diversity

Inventive Principle:

Principle #15Dynamics

Solution Approach 2:

The system changes parameters of content delivery by introducing diversity control mechanisms that modify recommendation parameters, such as exploration-exploitation balance, to simultaneously achieve relevant and diverse content presentation

Inventive Principle:

Principle #35Parameter changes

2Measurement precision

If recommendation engines analyze user behaviors to provide personalized content, then content accuracy is improved, but user content exploration deteriorates

Engineering Contradiction:

Improvecontent accuracyVSAvoiduser content exploration

Core Design Contradiction:

Measurement precisionVSEase of operation

Solution Approach 1:

The system introduces an intermediary layer between the recommendation engine and user that facilitates controlled exploration, allowing users to discover content beyond their established preferences while maintaining accurate personalized recommendations through the mediating interface

Inventive Principle:

Principle #24Intermediary (Mediator)

3Productivity

If systems provide personalized content based on user profile, then user engagement is improved, but content bias increases

Engineering Contradiction:

Improveuser engagementVSAvoidcontent bias

Core Design Contradiction:

ProductivityVSObject-generated harmful factors

Solution Approach 1:

The system applies preliminary anti-action by proactively counteracting content bias through debiasing algorithms that adjust recommendations before presentation, preventing the formation of echo chambers while maintaining user engagement through balanced content delivery

Inventive Principle:

Principle #9Preliminary anti-action

Data Source

PatentUS12332952B2System and method for un-biasing user personalizations and recommendations

Publication Date: 2025.06.17 VERIZON PATENT & LICENSING INC

AI summary

Disclosed are systems and methods for an electronic framework that enables un-biasing of personalizations for users. The disclosed framework provides controls that can enable users to selectively escape from previously conceived notions of a user's preferred tastes and/or interests. Upon a user requesting content, the disclosed framework can analyze the type of request as well as the modeled behavior and preferences of the user, and automatically un-bias or depersonalize content for the user, thereby availing the user to a broader range of content from a larger pool of content then previously made available to the user.