Influence-Based Advertising Targeting via Social Graph Analysis
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Solution Overview
Problem
Current methods for obtaining information to assist in decision-making lack a reliable measure of the trustworthiness of data sources, leading to subjective reputation assessments and inefficient advertising targeting, as they rely on proxy measures like demographics rather than direct influence estimation.
Innovation Solution
The development of a system that uses a social graph to estimate the influence of individuals and entities based on their connections and opinions, allowing for objective measurement and application in advertising pricing and targeting, where influence scores determine the relevance and cost of advertisements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional advertising targeting methods using demographic data are used, then advertising coverage is achieved, but advertising precision and effectiveness deteriorate due to reliance on proxy measures rather than direct influence estimation
Solution Approach 1:
The patent introduces an intermediary influence measurement system that acts as a mediator between advertisers and target audiences. This system uses social graph analysis and reputation scoring as intermediate steps to translate complex social influence data into actionable advertising targeting metrics, thereby improving precision without proportionally increasing system complexity
Solution Approach 2:
The patent replaces traditional mechanical demographic segmentation methods with computational social network analysis. Instead of using physical census data and demographic proxies, the system substitutes these with automated influence scoring based on social graph algorithms, reputation metrics, and digital footprint analysis, achieving higher precision through information-based rather than mechanical classification
2Quantity of substance
If more information is made available through electronic communications, then decision-making resources increase, but information reliability and trustworthiness deteriorate due to the sheer volume of unverified data
Solution Approach 1:
The patent applies local quality by differentiating information reliability based on the specific characteristics of each information source within the social graph. Instead of treating all information equally, the system assigns varying levels of trustworthiness to different nodes (individuals, organizations, sources) based on their reputation scores, influence metrics, and historical accuracy, thereby maintaining high information availability while ensuring reliability through source-specific quality assessment
Solution Approach 2:
The patent implements feedback mechanisms where the system continuously monitors and updates the reliability assessments of information sources based on user interactions, verification outcomes, and performance metrics. This creates a dynamic feedback loop where information trustworthiness is not static but continuously refined based on actual performance data, allowing the system to maintain high information availability while progressively improving reliability through learned patterns
3Adaptability or versatility
If subjective reputation assessments are used for advertising targeting, then personalization is achieved, but objectivity and consistency deteriorate due to lack of standardized influence measurement
Solution Approach 1:
The patent transforms subjective reputation assessments into objective measurements by changing the parameters from qualitative judgments to quantitative metrics. The system defines specific measurable parameters such as influence score, engagement rate, network centrality, and reputation index, converting personalization from a subjective art into an objective science based on standardized computational metrics that can be consistently applied across different contexts
Data Source
AI summary
Advertising based on influence is provided. In some embodiments, advertising based on influence includes determining an influence score (e.g., based on a given dimension) for a subject (e.g., a user), in which the subject is a potential target for an advertisement; and determining targeting of the advertisement based on criteria including the influence score of potential recipients of the advertisement. In some embodiments, the influence score is a directly estimated objective measure of influence (e.g., estimated using a social graph). In some embodiments, advertising based on influence also includes determining pricing of advertisements based on criteria including the influence score of potential recipients of one or more advertisements. In some embodiments, advertising based on influence further includes sharing advertising revenue with the subject based on criteria including the influence score of the first subject (e.g., as an incentive for the subject to view the advertisement).