Search Result Ranking Using Entity Trust Factors
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Solution Overview
Problem
Current search engines fail to effectively incorporate user trust relationships and annotations into search result rankings, leading to irrelevant results as they rely on weak indicators of user intent and lack of reputation-based information from vertical knowledge sites.
Innovation Solution
A search engine system that ranks results based on trust factors associated with entities providing annotations, using user-provided trust relationships and annotations to adjust document scores, thereby providing trust-adjusted search results.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If search engines use traditional ranking methods based on query terms and basic relevance, then the search process is simple and fast, but the search results fail to capture user intent and provide relevant information
Solution Approach 1:
The system performs preliminary actions by pre-computing trust scores for entities and pre-identifying annotations before the actual search query is processed. This allows the search engine to quickly apply pre-calculated trust factors to rank results without adding significant complexity to the real-time search process, thereby improving result relevance while maintaining system efficiency
Solution Approach 2:
The patent introduces trust scores as an intermediary metric that mediates between traditional relevance measures and final search result ranking. These trust scores, derived from entity relationships and annotations, act as a intermediary layer that refines result ordering without requiring complete system redesign, thus improving precision while managing complexity
2Measurement precision
If search engines incorporate trust relationships and annotations from multiple entities, then the accuracy of search results improves, but the computational complexity and data processing requirements increase
Solution Approach 1:
The system pre-computes trust scores for entities based on their relationships and annotations before search queries are submitted. This preliminary computation allows the actual search process to simply apply these pre-calculated scores, significantly reducing real-time computational energy requirements while maintaining high result accuracy
Solution Approach 2:
The patent applies trust factors selectively to search results based on query context and result relevance, rather than uniformly processing all possible entities. This partial application of trust analysis reduces computational overhead while still achieving accurate results where most needed
3Adaptability or versatility
If search engines rely on weak indicators such as static user preferences and predefined query reformulation, then the system is easy to implement, but it cannot fully capture variable user intent
Solution Approach 1:
The patent introduces entity trust scores as an intermediary that bridges static system parameters and dynamic user intent. These trust scores provide adaptability by allowing the system to flexibly weight different sources and annotations based on their credibility, capturing variable user intent without requiring complex real-time user modeling
Solution Approach 2:
The system dynamically adjusts ranking parameters based on entity trust scores rather than using fixed weighting schemes. This allows the search engine to adapt to different user intents by changing the importance weight of various search signals based on the credibility of their sources, achieving versatility through parameter flexibility
Data Source
AI summary
A search engine system provides search results that are ranked according to a measure of the trust associated with entities that have provided labels for the documents in the search results. A search engine receives a query and selects documents relevant to the query. The search engine also determines labels associated with selected documents, and the trust ranks of the entities that provided the labels. The trust ranks are used to determine trust factors for the respective documents. The trust factors are used to adjust information retrieval scores of the documents. The search results are then ranked based on the adjusted information retrieval scores.