Feedback Enabled Network Curation of Content Threads
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Solution Overview
Problem
Digital publishing platforms face challenges in filtering out irrelevant comments from users, leading to a decrease in the quality of feedback received by content creators, as many users receive unwanted comments and miss valuable insights from specific friends with relevant expertise.
Innovation Solution
A computer-implemented method that identifies and notifies a curated list of users based on their previous comments' relevance and satisfaction level, using an iterative feedback process to improve the quality of comments by selectively notifying users who have provided valuable feedback in the past, thereby enhancing the user's experience with more relevant and useful comments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If all users are notified to comment on published content, then the quantity of comments increases, but the quality of comments decreases due to irrelevant feedback from users without relevant expertise
Solution Approach 1:
The system performs preliminary analysis of user profiles, expertise areas, and comment histories before content publication. This advance preparation enables selective notification of only those users whose expertise matches the content topic, ensuring high-quality comments are solicited in advance rather than relying on random user engagement after publication.
Solution Approach 2:
The system implements a feedback mechanism that monitors user satisfaction with received comments and uses this information to refine future notification selections. By continuously learning from user responses and comment quality metrics, the system improves its ability to identify and notify the most relevant users for each piece of content, thereby maintaining high comment quality while managing notification quantities.
2Manufacturing precision
If a curated list of users is notified based on previous satisfaction levels, then the quality of feedback improves, but the device complexity increases due to iterative assessment and updating mechanisms
Solution Approach 1:
The system employs automated algorithms that independently assess user satisfaction levels, analyze comment relevance, and update the curated user lists without requiring manual intervention. The iterative assessment and updating mechanisms operate autonomously, using predefined criteria and machine learning techniques to maintain the curated lists, thereby reducing the operational complexity despite the sophisticated curation logic.
Solution Approach 2:
The system dynamically adjusts parameters such as satisfaction thresholds, notification frequencies, and user selection criteria based on accumulated data and performance metrics. By changing these parameters iteratively rather than maintaining fixed complex rules, the system achieves high feedback quality while managing computational complexity through adaptive simplification.
3Ease of operation
If irrelevant comments are filtered out, then the user experience improves, but the loss of potentially valuable information increases
Solution Approach 1:
The system applies different quality standards and filtering criteria to different types of comments based on their source, content, and relevance indicators. Rather than uniformly filtering or accepting all comments, the system evaluates each comment locally against multiple criteria including user expertise, comment relevance to specific content aspects, and potential value to the author, thereby preserving valuable insights while filtering out genuinely irrelevant feedback.
Solution Approach 2:
The system introduces an intermediary layer of intelligent analysis between comment submission and user reception. This intermediary mechanism uses natural language processing, topic modeling, and user profile matching to assess comment quality and relevance, acting as a mediator that preserves valuable diverse perspectives while filtering out spam and completely irrelevant comments, thus balancing user experience improvement with information preservation.
Data Source
AI summary
A computer-implemented method includes identifying, by a computer device, particular users of a digital publishing platform, the particular users being users that have commented on previous content published by a first user; identifying, by the computer device, correlated users, the correlated users being those particular users that have published a pertinent comment; assessing, by the computer device, a satisfaction level of the first user with each pertinent comment; identifying, by the computer device and based on the assessing, a set of users of the correlated users, each of the correlated users in the set of users having published a pertinent comment having a satisfaction level above a threshold; notifying, by the computer device, the set of users that the first user has published new content; detecting, by the computer device, new comments by the correlated users in the set of users, the new comments pertaining to the new content; assessing, by the computer device, a satisfaction level of the first user with each new comment; and updating, by the computer device, the set of users based on the satisfaction level of the first user with each new comment.