ML Feedback Suggestion System for User Interaction Descriptiveness
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Solution Overview
Problem
Existing systems for providing feedback between users are limited, resulting in minimal and non-descriptive feedback that does not effectively enhance user reputation or assist third users in deciding on interactions.
Innovation Solution
A method and system that utilize an interactive user interface and machine learning to suggest descriptive feedback to users based on their previous feedback patterns, writing styles, and user behavior, allowing users to rate suggested tags and provide feedback in a style consistent with their previous interactions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If users provide feedback in existing systems, then feedback is collected, but the feedback is minimal and non-descriptive
Solution Approach 1:
The system uses feedback from users' previous interactions and written styles to generate suggested feedback tags. The machine learning model analyzes historical feedback data and user writing patterns to automatically propose descriptive feedback tags, reducing the effort required while improving feedback quality and descriptiveness.
Solution Approach 2:
The system enables users to serve themselves by automatically generating feedback suggestions based on their own previous feedback patterns and writing styles. The machine learning model captures user-specific writing characteristics and uses them to create personalized feedback tag suggestions, allowing users to obtain high-quality feedback without significant manual effort.
2Reliability
If minimal feedback is provided, then feedback collection is simple, but user reputation enhancement is limited
Solution Approach 1:
The system implements a feedback loop where users' historical feedback and writing styles are continuously analyzed to improve future feedback suggestions. This enables the generation of more descriptive and reputation-enhancing feedback while maintaining ease of use, directly addressing the need for better user reputation through improved feedback quality.
3Loss of information
If no descriptive feedback is provided, then the system operates simply, but third users cannot make informed interaction decisions
Solution Approach 1:
The machine learning model acts as an intermediary between raw user feedback data and the feedback tags presented to users. It processes historical feedback and writing style information to generate descriptive tags that provide third users with the information needed to make informed interaction decisions, while keeping the user interface relatively simple.
Solution Approach 2:
The system automatically generates descriptive feedback tags using machine learning models that analyze user-specific writing patterns and historical feedback. This self-service approach provides rich information for third users without requiring complex manual input processes, balancing information quality with system simplicity.
Data Source
AI summary
A method for suggesting feedback is provided. The method includes determining an interaction between a first and second users, generating a feedback response element, and providing an interactive user interface that lists the feedback response element and a selectable machine learning feedback response element. The method also includes receiving a selection of the selectable machine learning feedback response element and accessing a first feedback associated with the first user where the first feedback has characteristics unique to the first user. Moreover, the method includes automatically generating a second feedback using the first feedback, the second feedback incorporating the characteristics unique to the first user without input from the first user.