Quality Score Propagation via Neighbor Features
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Solution Overview
Problem
Current search engines rely solely on individual resource features for ranking search results, neglecting the quality of linked resources, which can lead to suboptimal user satisfaction in search outcomes.
Innovation Solution
A quality management system that calculates and propagates quality scores for entities by incorporating neighbor features and entity-specific features, using a trained quality model to adjust scores based on linked resources, ensuring that search results reflect the quality of both the individual resource and its linked entities.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If search engines rely solely on individual resource features for ranking, then the ranking process is simple and fast, but the search result relevance and user satisfaction deteriorate
Solution Approach 1:
The patent introduces a quality score as an intermediary metric that mediates between individual resource features and search result ranking. The quality score aggregates multiple features (content quality, domain authority, user engagement) into a single comprehensive metric that can be used for ranking without requiring complex real-time analysis of all individual features for each search query.
Solution Approach 2:
The patent performs preliminary quality evaluation by pre-calculating quality scores for resources based on their features and neighbor entity qualities before actual search operations. This preliminary action allows the search engine to store and reuse these quality scores, avoiding the need to re-evaluate all features during each search, thus improving both relevance and efficiency.
2Measurement precision
If search engines consider only individual resource features, then the evaluation process is quick, but the quality assessment accuracy deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the quality of neighbor entities (linked resources) feeds into the quality assessment of the target entity. By considering the qualities of linked resources as feedback signals, the system achieves more accurate quality assessment, as high-quality entities tend to link to other high-quality entities. This feedback loop improves accuracy without requiring time-consuming manual verification.
Solution Approach 2:
The system performs preliminary quality assessments and stores results in advance. When evaluating an entity's quality, the system retrieves pre-computed quality scores of neighbor entities from storage rather than recalculating them, significantly reducing evaluation time while maintaining high assessment accuracy through the accumulated feedback from multiple evaluation cycles.
3Reliability
If search engines use comprehensive quality models considering neighbor features, then search result quality improves, but the computational complexity increases
Solution Approach 1:
The patent segments the quality evaluation process into distinct components: individual resource feature evaluation, neighbor entity quality evaluation, and aggregated quality scoring. Each component is handled separately and systematically, allowing the complex overall task to be managed through modular processing steps, reducing computational complexity while maintaining comprehensive assessment.
Solution Approach 2:
The patent transforms the complex multi-dimensional quality assessment problem into a simplified parameter-based system by introducing the quality score as a key parameter. The system adjusts and optimizes specific parameters (feature weights, neighbor influence factors) to achieve reliable search outcomes without requiring equally complex computational models for each aspect, balancing reliability with manageable complexity.
Data Source
AI summary
Methods, systems, and apparatus for computing quality scores based on neighbor features. In one aspect, a method includes obtaining a quality model that was trained using a set of training entities; identifying a set of candidate entities that are different from each of the training entities; for each candidate entity: obtaining a first quality score for the candidate entity; obtaining one or more neighbor features for neighbor entities of the candidate entity, where each neighbor entity of the candidate entity is linked to the candidate entity; obtaining one or more entity specific feature values for the candidate entity, where each entity specific feature value is determined independent of the neighbor entities of the candidate entity; and determining a second quality score for the candidate entity using the quality model, the second quality score being computed based on the first quality score, the neighbor features, and the entity specific feature values.