Ad targeting system using semantic clustering for precise audience matching
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Solution Overview
Problem
Existing ad networks rely on manually defined and inflexible vertical categories for targeting ads, which are inefficient and fail to accurately reach specific audiences, leading to wasted ad budgets and suboptimal ad placement.
Innovation Solution
The system allows advertisers to define and organize taxonomy categories using keywords and document properties, determining semantic clusters to suggest relevant verticals or properties for targeted ad placement, enabling more granular and effective ad targeting.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If manually defined vertical categories are used for ad targeting, then ad placement is simplified, but targeting precision deteriorates
Solution Approach 1:
The system automatically generates vertical categories using unsupervised learning algorithms that analyze website content and user behavior data. This self-service approach eliminates manual categorization while simultaneously improving targeting precision by creating granular, data-driven categories that reflect actual website characteristics and audience preferences.
Solution Approach 2:
The system dynamically adjusts vertical category parameters based on learned patterns from website content, user interactions, and engagement metrics. By changing the categorization parameters from static manual definitions to dynamic data-driven parameters, the system achieves both operational simplicity and high targeting precision.
2Measurement precision
If granular vertical categories are created to improve targeting, then ad relevance improves, but system complexity increases
Solution Approach 1:
The system employs unsupervised learning algorithms that automatically discover and create granular vertical categories without human intervention. This self-service mechanism handles the complexity of creating detailed categorizations internally while presenting a simplified interface to advertisers, thus achieving high targeting precision without increasing perceived system complexity.
Solution Approach 2:
The patent replaces manual mechanical categorization processes with automated machine learning systems. This substitution handles the computational complexity of creating and maintaining granular vertical categories through algorithms that automatically analyze website content, user behavior, and engagement patterns, reducing the need for manual system management while improving targeting precision.
3Ease of manufacture
If predefined verticals are used, then ad network implementation is easier, but ad budget efficiency deteriorates
Solution Approach 1:
The system automatically generates optimized vertical categories through unsupervised learning, eliminating the need for manual ad network configuration while improving ad budget efficiency. The self-service categorization system continuously learns from data to create optimal targeting groups, ensuring advertisers reach relevant audiences without manual intervention, thus reducing wasted ad spend while maintaining implementation simplicity.
Solution Approach 2:
The system incorporates feedback loops that continuously monitor ad performance, user engagement, and conversion metrics to refine vertical category definitions. This feedback mechanism ensures that the automatically generated verticals continuously improve ad budget efficiency by identifying and eliminating ineffective targeting, while the automated nature maintains implementation ease.
4Measurement precision
If manual vertical maintenance is performed, then category accuracy is maintained, but time consumption increases
Solution Approach 1:
The system performs automatic vertical maintenance through unsupervised learning algorithms that continuously analyze website content changes, user behavior patterns, and engagement metrics. This self-service maintenance mechanism preserves category accuracy by dynamically adapting to changing website characteristics and audience preferences without requiring manual intervention, thus eliminating time consumption while maintaining precision.
Solution Approach 2:
The patent implements continuous automated monitoring and updating of vertical categories through unsupervised learning processes that operate continuously in the background. This continuous action ensures category accuracy is maintained at all times by constantly adapting to new data, eliminating the need for periodic manual maintenance and the associated time consumption.
Data Source
AI summary
One or more keywords and/or information about one or more properties may be accepted, and a set of one or more taxonomy categories may be determined using at least some of the keyword(s) and/or property information and perhaps term co-occurrence clusters. The determined taxonomy categories may be presented to an advertising user as an ad targeting suggestion. Each taxonomy category may have at least one associated property (e.g., Web document), that participates in an advertising network. An advertiser selection of a suggested taxonomy category may be accepted, and the serving of an ad of the advertiser may be targeted to each property associated with the selected suggested taxonomy category. Alternatively, such properties may be presented to an advertising user as an ad targeting suggestion.