Personalized Entity Ranking via User-Specific Quality Weights
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing quality scoring systems for services and products fail to provide personalized quality evaluations, as they do not account for user-specific characteristics that influence quality perceptions, leading to inaccurate assessments based on general feedback.
Innovation Solution
A method that involves obtaining quality feedback with scores and text comments, identifying key characteristics, determining their influence on quality scores, and adjusting scores based on user-specific importance weights to generate personalized quality scores.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If general quality feedback is used to evaluate entities, then the evaluation process is simple and quick, but the accuracy and relevance of quality assessments deteriorate because they do not account for user-specific characteristics
Solution Approach 1:
The patent applies local quality by customizing quality scores for each user based on their specific characteristics and preferences. Instead of using a uniform quality metric for all users, the system adjusts quality scores individually to reflect what matters most to each user, thereby improving measurement precision without requiring a completely new evaluation system
Solution Approach 2:
The system changes the parameters used in quality assessment by incorporating user-specific characteristics (such as age, location, preferences) as weighting factors. This transforms the quality score calculation from a static general metric to a dynamic parameter-adjusted score that adapts to individual user profiles, improving accuracy while maintaining system functionality
2Measurement precision
If personalized quality scores are computed for each user, then the relevance and accuracy of evaluations improve, but the computational complexity and processing time increase
Solution Approach 1:
The system performs preliminary action by pre-calculating and storing user characteristics and their importance weights before actual quality assessments are needed. User profiles are built in advance based on their preferences and characteristics, so that when quality scores are needed, the system can quickly retrieve and apply pre-established parameters rather than computing everything from scratch, thus improving relevance while maintaining computation speed
Solution Approach 2:
The patent uses copying by creating a standardized quality score calculation framework that can be efficiently replicated for each user. The same core algorithm is applied to all users, with only the input parameters (user characteristics and weights) varying. This allows personalized scores to be generated using a consistent, optimized computation process that maintains speed while achieving individualization
3Measurement precision
If multiple user characteristics are considered in quality evaluation, then the comprehensiveness and accuracy of assessments improve, but the complexity of data processing and analysis increases
Solution Approach 1:
The system applies segmentation by dividing the quality evaluation process into distinct modular components: (1) collecting user characteristics, (2) determining importance weights for each characteristic, (3) retrieving entity quality data, and (4) calculating personalized scores. This segmentation allows complex multi-characteristic evaluation to be broken down into manageable steps, improving comprehensiveness while reducing processing complexity through structured organization
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, for providing personalized evaluations of products, services, or providers are disclosed. In one aspect, a method includes obtaining, for an entity, quality feedback including quality scores representing measures of quality for the entity and including feedback text submitted with the quality scores. A characteristic of the entity is identified from the feedback text, and an influence of the characteristic on the quality scores is determined. A quality profile specifying a measure of importance of the characteristic to the user is identified. An estimated quality value for the entity is determined based on the influence of the characteristic and the characteristic weight, and the entity is ranked based on the estimated quality score.