Social Bookmarking Ad Matching via User Tags
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Solution Overview
Problem
Current web-based advertising methods, such as Google's AdSense, do not optimally match ads to content, leading to suboptimal ad placement and reduced click-through traffic, as they rely on automated contextual and keyword analysis rather than user-defined classifications.
Innovation Solution
A social bookmarking system that collects user-generated classification data, such as tags, to determine ad placement, allowing users to create and store classifications that reflect their subjective associations with content, and uses these classifications to select relevant advertisements, either alone or in combination with conventional contextual mechanisms.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If automated contextual and keyword analysis is used to match ads to content, then ad placement can be implemented with minimal effort, but ad matching accuracy is suboptimal
Solution Approach 1:
The patent combines automated contextual analysis with user-generated tags to determine ad placement. The system merges multiple classification sources (automated content analysis, user tags, and user actions) to create a more accurate and comprehensive understanding of content, thereby improving ad matching accuracy while maintaining ease of implementation through automated processing of these combined signals.
2Measurement precision
If user-generated classification data is collected and used for ad placement, then ad matching accuracy improves, but system complexity increases
Solution Approach 1:
The social bookmarking system performs multiple functions: it allows users to bookmark content, generate tags for organization, and simultaneously provides this classification data for ad placement determination. By making the user-generated classification system multi-functional (serving both user organization needs and advertising matching needs), the patent avoids adding separate complexity while improving ad matching accuracy.
3Reliability
If user actions and information are used to determine ad placement, then ad relevance increases, but data processing requirements increase
Solution Approach 1:
The system extracts only the necessary classification signals from user actions and information (such as tags applied to bookmarks, search queries, and browsing patterns) rather than processing all raw user data. This extraction approach maintains high ad relevance by focusing on meaningful user-generated classifications while reducing the overall data processing volume through selective use of user information.
Data Source
AI summary
One aspect relates to a social bookmarking system that has the capability of displaying advertisements based on user input provided to the system. Advertisements displayed to the user may be determined based on one or more classifications provided by the social bookmarking system and selected by the user. Further, classification information that is created, used, or otherwise associated with the particular user may be used to determine ads displayed to that user. Another aspect relates to a system for collecting user classifications of content and using such classifications to match ads to appropriate content. In such a manner, more appropriate associations between ads and content may be made. Such classifications may be collected, for example, using a social bookmarking system. Further, advertisers may subscribe to classifications created in the social bookmarking system, allowing their advertisements to be displayed to users that perform actions with the subscribed classification. Also, advertisers may be permitted to submit their own classification information to the social bookmarking system.