Content Selection System Using Negative Signal Feedback
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Solution Overview
Problem
Existing content delivery systems face challenges in selecting and presenting content to users effectively, leading to revenue losses due to user disinterest, as they fail to accurately predict user preferences and respond to negative signals, resulting in content being closed or ignored.
Innovation Solution
A method and system that utilize user profiles, bid values, and probabilistic models to select content for presentation based on the likelihood of user engagement, incorporating user feedback to adjust content item scores and reduce negative signals, thereby optimizing content delivery and revenue generation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If content items are selected based on traditional methods without considering negative signals, then the content delivery process is simple, but revenue is lost due to user disinterest and content being closed or ignored
Solution Approach 1:
The system implements feedback mechanisms by receiving negative signals from client devices when users close or ignore content items. This feedback is stored in user profiles and used to adjust content item scores, creating a closed-loop system that continuously learns from user interactions to improve content selection and reduce revenue loss.
Solution Approach 2:
The system performs preliminary actions by pre-calculating content item scores that incorporate negative signal probabilities before content is presented to users. User profiles are pre-populated with negative signal data, and content item scores are adjusted in advance based on predicted user disinterest, allowing the system to proactively avoid presenting content likely to be closed or ignored.
2Productivity
If the system incorporates user feedback and probabilistic models to select content, then user engagement increases and revenue loss decreases, but the system complexity increases
Solution Approach 1:
The system changes parameters by dynamically adjusting content item scores based on multiple factors including bid values, click probabilities, and negative signal probabilities. User profiles store historical negative signal data that modifies future content selection parameters, allowing the system to adapt content delivery efficiency through parameter optimization rather than complex structural changes.
Solution Approach 2:
The system segments the content selection process into distinct components: user profile management, negative signal collection, probability calculation, bid value integration, and content item scoring. This segmentation allows each component to be optimized independently while working together to improve overall content delivery efficiency without proportionally increasing system complexity.
3Measurement precision
If the system uses multiple probabilities and bid values to score content items, then content selection accuracy improves, but computational requirements increase
Solution Approach 1:
The system applies partial action by selectively incorporating negative signal probabilities into content item scoring based on content type and user context. Not all content items require full probabilistic analysis - the system adjusts the depth of analysis based on bid values and user profile data, performing comprehensive scoring only when necessary to maintain selection accuracy while reducing unnecessary computational energy consumption.
Data Source
AI summary
One or more computing devices, systems, and/or methods for selecting content items for transmission to client devices are provided. A request for content associated with a client device may be received. Bid values and/or click probabilities associated with content items may be determined. A probability of receiving a negative signal associated with a content item of the content items from the client device responsive to presenting the content item via the client device may be determined based upon a user profile associated with the client device. A content item score, of content item scores associated with the content items, may be generated based upon the probability, a click probability and/or a bid value associated with the content item. The content item may be selected from the content items for presentation via the client device based upon the content item scores. The content item may be transmitted to the client device.