Content Recommendation Feedback Using Predictability Scores
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Solution Overview
Problem
Existing content recommendation systems struggle to accurately determine the success of recommendations and adjust based on viewer behavior, as viewers' preferences are diverse and complex, making it difficult to provide tailored content suggestions.
Innovation Solution
A system that determines a predictability score for content recommendations based on viewer history, updates the score based on viewer engagement with the recommended content, and adjusts future recommendations accordingly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If content recommendations are provided to viewers, then viewers receive customized content suggestions, but it is difficult to determine if the recommendations were successful or how successful they were
Solution Approach 1:
The patent implements a feedback mechanism by tracking viewer engagement metrics (completion rates, re-watches, pausing behavior) to determine whether recommendations were successful. This closed-loop feedback system allows the platform to measure recommendation effectiveness and use this information to improve future recommendations, resolving the contradiction between providing personalized content and measuring recommendation success.
2Adaptability or versatility
If viewers are provided with content recommendations based on viewing history, then content personalization is improved, but the complexity of determining viewer preferences increases
Solution Approach 1:
The patent simplifies preference analysis by changing the parameters used to measure viewer preferences from complex qualitative preferences to quantifiable engagement metrics such as completion rates, re-watch frequency, and pausing behavior. This transformation enables the system to maintain high content personalization while reducing the complexity of preference determination through objective, measurable parameters.
3Measurement precision
If the system tracks viewer engagement to update predictability scores, then recommendation accuracy is improved, but the amount of data processing and system complexity increases
Solution Approach 1:
The system implements self-service by automatically collecting, processing, and updating engagement metrics without requiring manual intervention. The predictability scores are dynamically updated based on real-time viewer engagement data, allowing the system to maintain high measurement precision while managing data processing complexity through automated workflows and efficient data collection mechanisms already inherent in the streaming platform infrastructure.
Data Source
AI summary
Methods and systems for content optimization are described. A computing device may determine a predictability score that indicates a probability that a device will access a first content item. The computing device may send a second content item associated with the first content item. The second content item may be based on the predictability score, and the predictability score may be modified. Additional content consumption and/or recommendations may be adjusted based on the predictability score.