Content Recommendation System Using Confidence Score Discrepancy

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Solution Overview

Problem

Existing content recommendation systems fail to account for the probability of future events, leading to inappropriate content recommendations that waste network resources and clutter user interfaces.

Innovation Solution

A system that calculates a user confidence score based on user behavior and metadata, and compares it to a prediction score from aggregated data, to generate content recommendations that align with the user's perceived probability of an event.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If content recommendations are generated based on accumulated user behavior data over time, then the system can provide personalized recommendations, but the recommendations may be inappropriate when event outcomes are important and consume network resources unnecessarily

Engineering Contradiction:

Improvepersonalization of recommendationsVSAvoidnetwork resource consumption

Core Design Contradiction:

Adaptability or versatilityVSLoss of energy

Solution Approach 1:

The system performs preliminary analysis by detecting key events in content and predicting their outcomes before generating recommendations. This allows the system to pre-determine whether to generate recommendations based on event significance, avoiding unnecessary network resource consumption while maintaining personalization for relevant content

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system incorporates user feedback mechanisms where users can indicate their interest in recommended content. This feedback loop allows the system to learn from user responses and refine its recommendation strategy, improving personalization while reducing waste by focusing on content users actually engage with

Inventive Principle:

Principle #23Feedback

2Quantity of substance

If content recommendations are generated without considering event outcome probability, then the system can present more content items, but the user interface becomes cluttered and important information is covered

Engineering Contradiction:

Improvenumber of recommended content itemsVSAvoiduser interface clarity

Core Design Contradiction:

Quantity of substanceVSEase of operation

Solution Approach 1:

The system applies different recommendation strategies to different content based on local characteristics. Content with significant events and high outcome probabilities receives targeted recommendations, while other content follows standard patterns. This localized approach maintains interface clarity by preventing overcrowding with irrelevant recommendations

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system dynamically adjusts recommendation parameters such as the number of items displayed and selection criteria based on event probability thresholds. When events are detected with high outcome probabilities, the system modifies recommendation behavior to present fewer, more relevant items, maintaining user interface clarity while preserving quantity for appropriate content

Inventive Principle:

Principle #35Parameter changes

3Duration of action of moving object

If the system presents content recommendations at all times based on user history, then user engagement can be maintained, but the timing may be inappropriate and user experience degraded

Engineering Contradiction:

Improveuser engagement continuityVSAvoidtiming appropriateness

Core Design Contradiction:

Duration of action of moving objectVSReliability

Solution Approach 1:

The system performs preliminary detection of events and prediction of outcomes before determining recommendation timing. By analyzing content metadata and event probabilities in advance, the system can identify optimal moments to present recommendations, ensuring both continuous engagement and appropriate timing based on predicted user interest

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system dynamically adjusts recommendation timing based on real-time analysis of user behavior patterns and predicted event outcomes. Rather than using fixed timing intervals, the system adapts recommendation presentation to match user engagement states and event significance, maintaining continuity while improving timing appropriateness

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS12231732B2Content recommendation based on a system prediction and user behavior

Publication Date: 2025.02.18 ADEIA GUIDES INC

AI summary

Systems and methods for generating a content item based on a difference between a user confidence score and a confidence score are disclosed. For example, a system generates for output a first content item. While the first content item is being outputted, the system receives user data via sensors of a device. The system determines a user confidence score based on the user data and metadata of the first content item. The user confidence score indicates a user's perceived probability of an event occurring in the future. The system calculates a prediction score which estimates the likelihood of the event occurring in the future. In response to determining that the difference between the user confidence score and the prediction score exceeds a threshold, the system selects a second content item related to the event and generates for output a recommendation comprising an identifier of the second content item.