Content Delivery System Using Performance History and Similarity Measures
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Solution Overview
Problem
Conventional content delivery systems face challenges in selecting appropriate contexts and invitational content for new content, often relying on random targeting due to lack of performance history, leading to inefficiencies and increased time and cost.
Innovation Solution
A system that combines performance history data with similarity measures to generate lists of potential contexts and invitational content, using a relational database to identify relevant clusters and rank values, enabling informed pairing of new invitational content with new contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If secondary content providers select primary content providers using random targeting due to lack of performance history knowledge, then the selection process is simple and quick, but the effectiveness and conversion rate of invitational content delivery deteriorates
Solution Approach 1:
The system performs preliminary actions by pre-calculating and storing performance history data, similarity measures, and rank values in a database before actual content delivery. This allows the system to make informed selections without real-time complex computations, resolving the contradiction between delivery effectiveness and system complexity.
Solution Approach 2:
The patent introduces an intermediary database that stores pre-computed performance history, similarity measures, and rank values. This intermediary structure mediates between the content providers and the selection algorithm, enabling efficient content-provider pairing without direct complex real-time analysis, thus improving productivity while managing system complexity.
2Measurement precision
If performance history data and similarity measures are combined to generate lists of potential contexts and invitational content, then the selection accuracy and conversion rate improve, but the data processing time and computational resources increase
Solution Approach 1:
The system performs preliminary computations of performance history analysis, similarity measures, and rank value calculations, storing results in a database before actual content delivery needs. This pre-computation approach enables accurate context selection without real-time processing delays, resolving the contradiction between measurement precision and time loss.
Solution Approach 2:
The system dynamically adapts its processing approach by using pre-computed data for routine selections and only performing additional real-time analysis when necessary. This dynamic behavior allows the system to maintain high selection accuracy while minimizing processing time for common operations.
3Adaptability or versatility
If new invitational content is paired with new contexts without performance history, then the system can quickly accommodate new content, but the reliability and expected performance of the pairing deteriorates
Solution Approach 1:
The patent introduces an intermediary database that stores performance history and similarity measures that serve as a mediator between new content and new contexts. Even when direct performance history is unavailable, the system uses this intermediary data structure to find analogous cases and make reliable pairings, thus maintaining reliability while accommodating new content.
Solution Approach 2:
The system uses copying by identifying similar existing content-context pairs and applying their performance patterns to new content. By copying successful pairing patterns from historical data, the system can reliably pair new invitational content with new contexts even without direct performance history, resolving the contradiction between adaptability and reliability.
4Loss of information
If the system uses a database to store performance history, content metadata, and context metadata, then the information availability and selection quality improve, but the database complexity and maintenance requirements increase
Solution Approach 1:
The patent segments the database into distinct modules: performance history data, content metadata, context metadata, similarity measures, and rank values. This segmentation allows the system to maintain comprehensive information availability while managing database complexity through modular organization, enabling targeted queries and reduced maintenance overhead.
Data Source
AI summary
Systems and methods are provided for selecting contexts for new invitational content and invitational content for new contexts. In particular, a performance history of delivered invitational content in known contexts is combined with similarity measures for the delivered invitational content, with respect to a new invitational content, to generate a list of potential contexts for the new invitational content. Similarly, a performance history of in known contexts with delivered invitational content can combined with similarity measures for known contexts, with respect to a new context, to generate a list of potential content for the new context. Further, a combination of these methods can be used to pair new invitational content with new contexts.