Markup Language Schema for Internal Ranking Models
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Solution Overview
Problem
Current search engine ranking functions are complex and inefficient, requiring improved representations to effectively sort documents by predicted relevance.
Innovation Solution
The architecture employs a markup language schema, such as XML, to represent internal ranking models, allowing for multiple ranking models like linear and neural networks, which process documents in multiple stages to enhance relevance scoring.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current ranking function representations are used, then documents can be sorted by predicted relevance, but the representation becomes complex and problematic
Solution Approach 1:
The patent creates a simplified copy or representation of the ranking function using a domain-specific language (DSL) that captures the essential logic and parameters without the complexity of internal implementation details. This DSL representation allows for easier manipulation, debugging, and optimization of ranking functions while maintaining their predictive accuracy.
Solution Approach 2:
The patent transforms the ranking function into a parameterized format where key components (such as boost factors, thresholds, and weighting coefficients) can be independently adjusted and optimized. This parameterization allows the system to maintain high relevance scoring accuracy while simplifying the representation through structured parameter definitions rather than complex computational graphs.
2Measurement precision
If multiple ranking models are implemented to enhance relevance scoring, then document ranking accuracy improves, but system complexity increases
Solution Approach 1:
The patent divides the ranking system into multiple independent models (e.g., neural network models, linear models, graph-based models) that can be developed, trained, and optimized separately. Each model handles specific aspects of relevance scoring, and their results are combined through a unified framework, allowing for improved accuracy without overwhelming complexity in any single model.
Solution Approach 2:
The patent creates a universal ranking framework that can accommodate multiple different model types and methodologies within a single system. This framework provides common infrastructure for model training, evaluation, and deployment, allowing the system to leverage diverse modeling approaches (neural networks, traditional machine learning, graph algorithms) while maintaining manageable complexity through shared components.
Data Source
AI summary
A markup language schema utilized to represent internal ranking models. In one implementation, the schema developed utilizes XML (extensible markup language) for internal ranking model representation. Other markups languages can be employed.