Content Recommendation Feedback Weighting for Sparse Viewing Histories
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Solution Overview
Problem
Content recommendation engines struggle to accurately recommend media content items when users have viewed a small amount of content or their viewing history does not sufficiently match other users', leading to inaccurate predictions.
Innovation Solution
Implement a feedback-based weighting system that adjusts the importance of user preferences and viewing history based on whether previously recommended content was selected or not, using flexible and adjustable weighting factors to refine recommendations over time.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If mathematical algorithms are used to compute predictive models for content recommendation, then content recommendations can be automated, but accuracy deteriorates when users have viewed relatively small amounts of content or when viewing history does not sufficiently match other users
Solution Approach 1:
The patent implements a feedback mechanism where user interactions with recommended content (selections, views, ratings) are captured and used to update the predictive model. This feedback loop allows the system to learn from actual user behavior patterns, continuously improving recommendation accuracy even for users with limited viewing history by adapting to their specific preferences over time.
Solution Approach 2:
The system dynamically adjusts model parameters based on user-specific characteristics and feedback data. By changing parameters such as weighting factors for different user behaviors, time decay rates, and similarity thresholds, the system optimizes recommendation accuracy for individual users with limited viewing history while maintaining automation.
2Adaptability or versatility
If recommendations are based on user viewing history matching, then personalized recommendations can be provided, but reliability deteriorates when viewing history is insufficient or does not match other users
Solution Approach 1:
The feedback mechanism captures actual user responses to recommendations and uses this information to refine the personalization model. By continuously learning from user selections and interactions, the system improves the reliability of personalized recommendations even when initial viewing history is insufficient or atypical, allowing the model to adapt to individual user patterns.
Solution Approach 2:
The system performs preliminary actions by pre-processing user viewing history data to identify patterns and preferences before generating recommendations. This preliminary analysis creates a foundation for reliable personalization by pre-organizing and interpreting user behavior data, making the system more robust to insufficient or unique viewing histories.
Data Source
AI summary
Media content may be recommended based on feedback weightings. Input data describing the presentation of a media content items in association with content management accounts may be represented as data points. Each data point may identify feedback data for a media content item previously recommended for presentation in association with a content management account. The feedback data may identify a viewer reaction to the recommended media content item. A weighting factor based on the feedback data for the media content item presented in association with the content management account may be applied to produce a recommendation.