Feedback Sensitivity Ranking for Content Creator Recommendations
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current online network services fail to provide relevant follow recommendations to users as they do not consider the interests of potential followers or content creators, leading to a poor user experience.
Innovation Solution
A system and method using machine learning to determine a feedback sensitivity measure for candidate content creators, ranking them based on expected feedback and content creation likelihood, while balancing the interests of both followers and content creators, to recommend high-quality followees.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If users are recommended to follow content creators with the most followers, then the network service can encourage more users to follow other users, but the recommendations become irrelevant to user interests and content creator interests
Solution Approach 1:
The patent changes the parameters used for ranking content creators from purely follower count-based to a multi-parameter system that includes feedback sensitivity measures. This feedback sensitivity is calculated based on the relationship between feedback received and content creation output, allowing the system to identify creators who are most responsive to audience engagement. By incorporating this additional parameter, the system achieves more relevant recommendations while still promoting active content creation.
Solution Approach 2:
The patent implements a feedback mechanism where user interactions (likes, comments, shares) are systematically collected and analyzed to calculate feedback sensitivity measures for each content creator. This feedback loop allows the system to continuously refine its recommendations by identifying creators whose content resonates with audiences, thereby improving recommendation relevance while maintaining high content creation output.
2Productivity
If content creators receive more feedback, then they are more likely to create more content, but the system complexity increases due to need for feedback analysis and ranking
Solution Approach 1:
The patent enables content creators to essentially rate themselves through their own feedback patterns. By analyzing the relationship between feedback received and content creation output, the system automatically identifies feedback-sensitive creators without requiring manual evaluation. This self-service approach allows the system to handle complexity automatically through algorithmic analysis rather than manual intervention.
Solution Approach 2:
The patent simplifies the complex feedback analysis process by transforming it into a calculable parameter - feedback sensitivity. This parameter condenses multiple feedback dimensions into a single measurable metric that can be directly used for ranking, thereby reducing system complexity while maintaining the ability to identify high-producing, feedback-responsive creators.
Data Source
AI summary
Techniques for nurturing content creation are provided. In one technique, a particular user is identified. Candidate entities are identified based on one or more attributes of the particular user. For each candidate entity, a feedback sensitivity measure of content creation of the candidate entity is determined. The feedback sensitivity measure is generated based on an amount of feedback, from other users, to content that the candidate entity has created. A score is then generated for the candidate entity based on the measure. A ranking of the candidate entities is determined based on the score of each candidate entity. A subset of the candidate entities is selected based on the ranking. The subset of the candidate entities is transmitted over a computer network to be presented on a computing device of the particular user.