User Characteristic Score Calculation Using Weighted Self and Other Evaluations
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Solution Overview
Problem
Existing systems for evaluating user characteristics in social networking services do not adequately differentiate between subjective and objective evaluations, leading to inaccurate final scores and reliability issues, particularly when malicious users intervene.
Innovation Solution
An information processing device that calculates user evaluation scores by applying weighting coefficients to both self-evaluation and other-evaluation scores, using a configuration that includes a self-evaluation score calculation unit, an other-evaluation score calculation unit, and a final evaluation score calculation unit, with the ability to set coefficients based on rules or machine learning, to optimize the evaluation of characteristic items.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If average points of self-evaluation and objective evaluation are calculated uniformly for all evaluation items, then the evaluation process is simple and easy to operate, but evaluation accuracy deteriorates because subjective and objective evaluations are not differentiated appropriately
Solution Approach 1:
The patent applies local quality by differentiating the weighting coefficients for self-evaluation and objective evaluation based on the specific evaluation item. Different evaluation items have different weighting schemes - some prioritize self-evaluation while others prioritize objective evaluation, allowing each item to be evaluated according to its specific characteristics rather than using a uniform approach.
Solution Approach 2:
The patent implements dynamics by making the weighting coefficients adjustable and adaptable rather than fixed. The system can dynamically adjust the weights of self-evaluation and objective evaluation based on the evaluation item type, user characteristics, and other factors, allowing the evaluation method to adapt to different scenarios while maintaining simplicity through automated adjustment.
2Reliability
If self-evaluation scores are used for all evaluation items, then user input is required for all items which increases data collection complexity, but evaluation reliability improves when other users' evaluations are incorporated
Solution Approach 1:
The patent merges self-evaluation and objective evaluation (evaluations by other users) into a unified final evaluation score. By combining these two evaluation sources with appropriate weighting coefficients, the system achieves higher reliability without requiring separate data collection processes for each evaluation type, as both sources are integrated through the existing evaluation framework.
Solution Approach 2:
The patent applies universality by creating a single evaluation framework that handles both self-evaluation and objective evaluation uniformly. The same evaluation structure and calculation method are used for both types of evaluations, eliminating the need for separate data collection and processing systems while maintaining the reliability benefits of multi-source evaluation.
3Productivity
If uniform weighting coefficients are applied to all evaluation items, then the calculation process is simple and fast, but measurement precision deteriorates because different evaluation items have different evaluation scales and characteristics
Solution Approach 1:
The patent applies parameter changes by adjusting the weighting coefficients based on the specific characteristics of each evaluation item. Different evaluation items have different weights assigned to self-evaluation and objective evaluation components, allowing the system to optimize precision for each item type while maintaining efficient calculation through automated parameter adjustment rather than manual intervention.
Data Source
AI summary
The information processing device includes an information storage unit, a data acquisition unit, and a user characteristic score calculation unit. The information storage unit stores information regarding the user and information regarding the actions of other users. The data acquisition unit acquires information regarding the characteristic item from the information storage unit. The user characteristic score calculation unit includes a self-evaluation score calculation unit, an other-evaluation score calculation unit, and a final evaluation score calculation unit. The self-evaluation score calculation unit calculates a self-evaluation score by the user. The other-evaluation score calculation unit calculates the other-person evaluation score by other users. The final evaluation score calculation unit calculates the user evaluation score based on the weighting coefficient, the self-evaluation score, and the other-person evaluation score.