Content Recommendation Using Positivity Index Balancing

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Solution Overview

Problem

Existing content recommendation systems often provide users with an excessive amount of content of a single type, failing to offer a balanced breadth of content types based on user characteristics.

Innovation Solution

A method and system that determine a positivity index score for an initial result set using a user profile, comparing it to a threshold to add content of a different type to balance the content set, utilizing a positivity analyzer engine and user knowledge graphs to refine content recommendations.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If the system recommends content based on user queries and initial results, then the content is relevant to user interests, but the content diversity and balance of different types deteriorates

Engineering Contradiction:

Improvecontent relevance to user interestsVSAvoidcontent type diversity

Core Design Contradiction:

Measurement precisionVSAdaptability or versatility

Solution Approach 1:

The system calculates a positivity index score based on the initial result set and user profile, then uses this score as feedback to determine what type of additional content to recommend. If the score indicates an imbalance (e.g., too much negative content), the system recommends content of the opposite type to restore balance, creating a feedback loop that maintains content diversity while preserving relevance.

Inventive Principle:

Principle #23Feedback

Solution Approach 2:

The system changes the recommendation parameter from purely relevance-based to a balanced approach that considers both relevance and content type distribution. By introducing the positivity index score as an additional parameter, the system adjusts content selection to achieve better diversity while maintaining user interest alignment.

Inventive Principle:

Principle #35Parameter changes

2Stability of the object's composition

If the system provides content of a single type based on current events and user queries, then the content is consistent with user concerns, but the breadth and variety of content types deteriorates

Engineering Contradiction:

Improvecontent consistency with user concernsVSAvoidcontent breadth

Core Design Contradiction:

Stability of the object's compositionVSAdaptability or versatility

Solution Approach 1:

The system intentionally introduces asymmetry in content recommendation by considering both the user's expressed interests and the overall positivity balance. Rather than symmetrically recommending only relevant content, the system asymmetrically adjusts recommendations to include content that balances the positivity index, thereby expanding content breadth while maintaining consistency with user concerns.

Inventive Principle:

Principle #4Asymmetry

3Productivity

If the system recommends more content of the same type to reinforce user interests, then the user interest alignment is improved, but the content balance and user experience quality deteriorates

Engineering Contradiction:

Improveuser interest alignment efficiencyVSAvoidexcessive single-type content impact

Core Design Contradiction:

ProductivityVSObject-affected harmful factors

Solution Approach 1:

The system applies a counterweight mechanism by calculating the positivity index score and recommending content of the opposite type when imbalance is detected. This counteracts the tendency to over-recommend content of a single type, preventing the harmful effect of excessive single-type content while maintaining efficient user interest alignment through the scoring mechanism.

Inventive Principle:

Principle #8Anti-weight (Counterweight)

Data Source

PatentUS12517918B2Systems and methods for automatically recommending content

Publication Date: 2026.01.06 ADEIA GUIDES INC

AI summary

Systems and methods are provided for automatically recommending content. One example method includes receiving, from a computing device, a request for content and generating an initial result set in response to the request. A user profile associated with the request is identified. Based on the initial result set and the user profile, a positivity index score for the initial result set is determined. Based on the positivity index score, a type of additional content to add to the initial result set is determined. A modified result set is generated by adding the additional content to the initial result set. At least a portion of the modified result set is transmitted to the computing device.