Pairwise Content Quality Prediction Model Using User Polling
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Solution Overview
Problem
Traditional performance metrics for online advertising do not effectively measure the quality of advertisements, as they are subjective and fail to determine which ads are more enjoyable for users, leading to difficulties in identifying high-quality ads that can go viral.
Innovation Solution
A social networking system uses user polling to collect pair-wise comparisons of content items, training a predictive model to score content items based on perceived quality through regression analysis and feedback coefficients, which are computed using statistical models and user feedback probabilities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional performance metrics (click through and conversion rates) are used to measure advertisement effectiveness, then the measurement process is simple and straightforward, but the quality of advertisements cannot be evaluated and subjective user enjoyment remains undetermined
Solution Approach 1:
The patent introduces an intermediary predictive model that mediates between simple user feedback data and advertisement quality assessment. The model uses machine learning algorithms to translate basic user interactions and survey responses into quality scores, avoiding the need for complex direct measurement while achieving precise quality evaluation
Solution Approach 2:
The patent replaces traditional mechanical survey methods (focus groups, manual rating) with an automated electronic predictive model. This substitution uses computational algorithms to process user feedback and generate quality measurements, achieving both precision and scalability without manual intervention
2Measurement precision
If focus groups are used to determine advertisement effectiveness, then subjective opinions can be gathered, but the sample size is small and cannot determine which advertisements are objectively higher quality
Solution Approach 1:
The predictive model serves multiple functions simultaneously: it processes diverse data types (clicks, conversions, survey responses, user demographics), applies multiple analysis techniques, and generates comprehensive quality assessments. This multi-functionality allows the system to leverage large volumes of varied user feedback to achieve precise quality differentiation
Solution Approach 2:
The patent transforms user feedback from qualitative subjective responses into quantitative measurable parameters. By converting user opinions, clicks, and engagements into numerical data points that can be statistically analyzed, the system processes large volumes of feedback to generate objective quality measurements with high precision
3Measurement precision
If advertisers rely on focus group effectiveness data, then they can determine if advertisements will be effective, but they cannot determine which advertisements are more enjoyable and likely to go viral
Solution Approach 1:
The patent implements continuous feedback loops where user interactions with advertisements (clicks, shares, time spent, survey responses) are constantly collected and fed back into the predictive model. This ongoing feedback mechanism allows the system to learn from user behavior patterns and accurately measure enjoyment and viral potential, preventing loss of quality information
Solution Approach 2:
The system enables users to self-report their enjoyment and engagement through integrated surveys and interaction tracking. Users directly provide feedback on what they find enjoyable without researcher intervention, and this self-generated data is automatically processed by the predictive model to preserve complete quality information
Data Source
AI summary
A social networking system presents content items to users, who then provide feedback regarding pairs of content items. The feedback includes a selection of a content item of the pair of content items that was preferred by the user over the other content item. The social networking system uses this information to train a predictive model that scores content items based on quality. The content items may be advertisements. The social networking system uses the pair-wise comparisons of the advertisements to determine feedback coefficients in an advertising quality score prediction model using regression analysis of the pair-wise comparisons for each predictive factor in the model. In this way, the pair-wise comparisons are used to train the prediction model to understand which advertisements are more enjoyable than others. A feedback coefficient for each predictive factor may be computed based on the preferences received from the group of users.