Search Scope Biasing for User-Specific Relevance Ranking
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Solution Overview
Problem
Traditional search engines lack the ability to customize ranking models based on user-specific knowledge and domain expertise, as they rely on a fixed evaluation dataset that may not represent all possible environments, leading to suboptimal relevance in specialized domains.
Innovation Solution
The system allows users to define search scopes that can influence ranking scores, converting these scopes into static features to bias the ranking process, enabling users to configure weights ad-hoc or using machine learning to optimize ranking based on their knowledge and preferences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If a fixed ranking model is used for all queries, then consistency in ranking is maintained, but the system cannot adapt to user-specific knowledge and domain expertise
Solution Approach 1:
The patent segments the ranking model into multiple components: a base ranking model that provides consistent general-purpose ranking, and user-defined scope models that provide specialized domain knowledge. Each scope model can be independently configured and applied based on user preferences, allowing the system to adapt to different domains without requiring a complete redesign of the ranking system.
Solution Approach 2:
The patent implements dynamic ranking by allowing the system to switch between different scope models based on user-defined preferences and query characteristics. The ranking model is no longer static but can be dynamically adjusted by selecting appropriate scopes that match the user's domain expertise or query context, enabling adaptability while maintaining operational simplicity.
2Reliability
If a single ranking model is applied to all queries, then the system is easy to operate, but it cannot capture user knowledge about specific corpora
Solution Approach 1:
The patent enables users to define their own scopes and preferences without requiring deep knowledge of the ranking algorithm internals. Users can specify scopes based on their domain knowledge (e.g., prioritizing certain websites, file types, or content patterns), and the system automatically integrates these preferences into the ranking process, making customization accessible to end-users while maintaining reliability.
3Adaptability or versatility
If the ranking model is fixed after product release, then the system is stable, but it cannot optimize for different evaluation datasets or environments
Solution Approach 1:
The patent allows users to pre-define scopes and preferences before executing searches. Users can prepare scope configurations based on their domain knowledge in advance (e.g., defining preferred sources, file types, or content patterns for their specific corpus), and these pre-configured scopes are then automatically applied during query processing, enabling adaptation to different environments without requiring real-time model changes.
4Ease of operation
If conventional search scopes define hard cut-off selection criteria, then the filtered set is well-defined, but the ranking cannot be biased by user preferences
Solution Approach 1:
The patent transforms scope definitions from hard cut-off criteria (binary inclusion/exclusion) into soft preference signals that influence ranking scores. Instead of simply filtering documents based on scope membership, the system uses scope information to adjust ranking parameters, allowing documents within scopes to receive ranking boosts while maintaining the clarity and simplicity of scope definitions for users.
Data Source
AI summary
A mechanism for expressing a user preference to a set of documents based on user knowledge about the document corpora. The user preference input to the system can be positive, negative, or both. A set of documents that can be identified with a query can define a search scope definition. The search scope is mapped into an input ranking feature for a ranking function. The search scope definition is employed as a soft preference ranking feature, and thus, used to bias ranking via relevance feedback. The mechanism facilitates increasing or decreasing the final ranking score of a document based on whether the document falls into the user scope. The ranking weight can be configured by the user ad-hoc, or when relevance judgments are available, using machine learning techniques to find the optimal weights to optimize ranking.