Disliked Content Ratings Matrix for Biased Recommendation Systems

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Solution Overview

Problem

Current content viewership systems fail to accurately determine disliked content, leading to biased recommendation systems and revenue loss due to channel surfing, as they primarily focus on tracking liked content and lack effective methods to handle disliked content.

Innovation Solution

A method and system that process channel viewing data to emphasize temporal dislike aspects, apply machine learning algorithms to generate a disliked content ratings matrix, and integrate this data into recommendation and alternate content generation systems to provide enhanced user experiences and prevent revenue loss.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If traditional affinity tracking techniques are used to track user preferences, then the system can identify liked content, but the recommendation system becomes inherently biased and fails to identify disliked content accurately

Engineering Contradiction:

Improveaccuracy of disliked content determinationVSAvoidbias in recommendation system

Core Design Contradiction:

Measurement precisionVSLoss of information

Solution Approach 1:

Instead of tracking liked content as traditional systems do, this patent inverts the approach by tracking disliked content through channel surfing detection. The system identifies content users actively avoid by detecting channel changes during content playback, creating an inverted preference signal that reveals negative user preferences rather than positive ones.

Inventive Principle:

Principle #13The other way round (Inversion)

Solution Approach 2:

The patent introduces channel surfing detection as an intermediary mechanism to indirectly measure disliked content. Rather than directly asking users what they dislike or relying on explicit ratings, the system uses channel change behavior as a mediator to infer negative preferences, allowing accurate identification of disliked content without direct user input.

Inventive Principle:

Principle #24Intermediary (Mediator)

2Ease of operation

If users are unable to avoid disliked content, then channel surfing occurs, but this leads to advertising revenue loss for the programmer

Engineering Contradiction:

Improveuser ability to avoid disliked contentVSAvoidadvertising revenue loss

Core Design Contradiction:

Ease of operationVSLoss of energy

Solution Approach 1:

The system performs preliminary action by detecting channel surfing behavior and identifying disliked content before significant revenue loss occurs. By analyzing channel change patterns in real-time or near-real-time, the system can proactively identify content users dislike and enable them to avoid it, preventing continued revenue loss rather than reacting after the fact.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The patent enables self-service by giving users automated assistance in avoiding disliked content. The system autonomously detects channel surfing, identifies disliked content, and can integrate with recommendation systems to prevent users from encountering content they are likely to dislike, allowing users to self-manage their viewing experience without manual intervention.

Inventive Principle:

Principle #25Self-service

3Adaptability or versatility

If recommendation systems only track liked content, then the system maintains simplicity, but it cannot provide enhanced user viewing experiences

Engineering Contradiction:

Improveenhanced user viewing experienceVSAvoidcomplexity of content analysis system

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The patent extracts only the essential elements needed for disliked content identification from complex user behavior data. Instead of analyzing all viewing metrics, the system focuses specifically on channel change timing and patterns during content playback, extracting the critical signal of channel surfing while filtering out unnecessary complexity.

Inventive Principle:

Principle #2Taking out (Extraction)

Solution Approach 2:

The system applies parameter changes by transforming raw channel viewing data into scaled temporal aspects that highlight dislike signals. Through data preprocessing and scaling operations, the system converts ordinary viewing metrics into enhanced features that emphasize channel surfing behavior, making disliked content identification more accurate without requiring fundamentally complex analysis infrastructure.

Inventive Principle:

Principle #35Parameter changes

Data Source

PatentUS11653064B2Methods and systems for determining disliked content

Publication Date: 2023.05.16 CHARTER COMM OPERATING LLC

AI summary

Methods and systems for determining and using disliked content are described. The method includes obtaining, by a service provider system, channel viewing data from a user device of a user, pre-processing, by a content analysis unit, the channel viewing data to mitigate inaccuracies and inconsistencies in the channel viewing data, scaling, by the content analysis unit, the pre-processed channel viewing data to highlight temporal dislike aspects of the pre-processed channel viewing data, applying, by the content analysis unit, machine learning algorithms to the scaled channel viewing data to generate a disliked content ratings matrix, and outputting, by the content analysis unit to user interface systems in the service provider system, to provide enhanced user viewing. The user interface systems including a recommender system, an alternate content generation system, or both.