Ad Delivery via Site Clustering and Audience Profiling
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Solution Overview
Problem
Existing ad targeting systems face challenges in effectively delivering ads to users and websites with niche audiences or unclear content classifications, as they often lack sufficient historical data for ad performance, leading to inadequate ad selection.
Innovation Solution
A method is implemented where a mathematical representation of user interactions with websites is computed, clustering similar sites based on audience profiles, allowing for ad selection based on performance history within these clusters for targeted ad delivery.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If ads are targeted based on content classification, then ad relevance to content is improved, but classification accuracy deteriorates for niche or politically neutral sites
Solution Approach 1:
The patent inverts the traditional content-based classification approach by using audience-based classification instead. Instead of analyzing web page content to determine topic, the system analyzes user interaction patterns and demographics to characterize sites. This inversion resolves the contradiction by avoiding content analysis limitations while maintaining ad relevance through audience targeting.
Solution Approach 2:
The patent introduces user audience characteristics as an intermediary between content and ad selection. Rather than directly matching ads to content categories, the system uses user demographics and interaction patterns as a mediator to bridge content sites and appropriate advertisements, enabling accurate targeting without relying on content classification accuracy.
2Measurement precision
If ads are targeted based on user classification, then ad targeting precision is improved, but data availability deteriorates for users with limited history
Solution Approach 1:
The patent merges individual user data with collective audience profile data to create a complementary targeting system. By combining user-level information with site-level audience characteristics from clustered sites, the system can provide precise targeting even when individual user history is limited, as the collective data fills in gaps for new or less-active users.
3Ease of manufacture
If content classification is used for ad targeting, then implementation simplicity is improved, but effectiveness deteriorates for niche audiences
Solution Approach 1:
The patent replaces the mechanical content analysis system with a data-driven audience profiling system. Instead of using web crawlers to scan and classify page content, the system uses statistical analysis of user interaction patterns, demographics, and site clustering algorithms to characterize audiences. This substitution maintains implementation feasibility while dramatically improving effectiveness for niche audiences through more sophisticated data processing.
Data Source
AI summary
For each of various sites, a mathematical representation is computed according to prescribed characteristics of users that have conducted one or more predetermined types of interaction with the site. Cluster of the sites are identified whose computed representations are similar according to prescribed criteria. Responsive to notification of an opportunity to deliver unidentified advertising to a given user via a given site, at least one ad is selected based upon factors including the ad having a prescribed performance history at one or more clusters containing the given site. The selected ad is transmitted to the given user at the given site, or a bid is transmitted for such placement of the ad.