Ad Quality Score Adjustment Using User Browsing History
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Solution Overview
Problem
Current online advertisement systems fail to effectively determine which advertisements to display to users based on their browsing history, leading to irrelevant ads being shown and inefficient auction processes.
Innovation Solution
A computerized method that adjusts the quality score of advertisements by considering user metrics such as clickiness, derived from browsing history, to balance between bid value and content quality in online auctions, thereby optimizing ad selection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of energy
If traditional advertisement systems display ads based solely on bid value, then revenue generation is maximized, but ad relevance to user interests deteriorates
Solution Approach 1:
The system changes the parameter of ad selection from purely bid-based to quality score-based, where quality score incorporates user browsing history, device type, and ad format preferences. This parameter change allows the system to maintain revenue generation while significantly improving ad relevance to user interests
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring user interactions with ads and using this information to adjust quality scores. User browsing history and engagement patterns provide feedback that refines future ad selections, creating a closed-loop system that improves both relevance and revenue over time
2Measurement precision
If advertisement quality scoring incorporates multiple user metrics, then ad relevance improves, but system complexity increases
Solution Approach 1:
The system segments the quality score calculation into distinct components: user browsing history analysis, device type identification, ad format preference detection, and relevance matching. Each segment is processed independently and then integrated, making the complex system more manageable and maintainable while preserving high ad relevance
Solution Approach 2:
The patent introduces intermediary components such as quality score calculators and user profile databases that mediate between raw user data and final ad selection decisions. These intermediaries simplify the overall system architecture by providing standardized interfaces and abstraction layers
3Adaptability or versatility
If real-time user metrics are analyzed for each auction, then ad personalization improves, but processing time increases
Solution Approach 1:
The system performs preliminary actions by pre-processing and storing user browsing history, device characteristics, and ad format preferences in user profiles before auctions occur. This advance preparation enables rapid quality score calculation during real-time auctions, achieving both high personalization and fast processing
Solution Approach 2:
The patent merges multiple data sources including browsing history, device type information, and ad format preferences into a unified quality score. This integration consolidates multiple processing operations into a single streamlined evaluation, reducing overall processing time while maintaining comprehensive ad personalization
Data Source
AI summary
A computerized method and apparatus for evaluating content on a computer network. The method includes obtaining a quality score of content configured for display with a web page, wherein the quality score is based at least in part on keywords associated with the content and either a search query or metadata associated with the web page. The method also includes identifying a user metric of a computing device associated with the search query or the metadata. The method further includes generating an adjusted quality score of the content based on the quality score and the user metric. The method also includes selecting a parameter for an auction based on the adjusted quality score, wherein the parameter indicates a relation between a bid value based auction and a content quality based auction.