Comment Ranking Using Interaction History Signals
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Solution Overview
Problem
Social networking platforms face challenges in effectively ranking and filtering user comments to enhance user experience, as existing methods fail to accurately identify and prioritize relevant, high-quality comments amidst a large volume of user contributions.
Innovation Solution
A method that assigns scores to comments based on signal values related to author reputation, content quality, audience interactions, and historical user impressions, using a combination of signals such as grammar quality, originality, relevance, and audience engagement to rank and filter comments, ensuring that high-quality comments are displayed prominently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If comments are ranked based on multiple signal values including author reputation, content quality, and audience interactions, then the quality and relevance of displayed comments is improved, but the computational complexity and processing time increases
Solution Approach 1:
The patent segments the comment ranking process into multiple independent signal components: author-related signals (reputation, history), content-related signals (grammar quality, originality, relevance), and audience-related signals (interactions, impressions). Each signal is calculated separately using specific formulas, allowing the system to maintain high measurement precision while managing complexity through modular computation. The final comment score is derived by combining these segmented signals.
2Ease of operation
If all user comments are displayed in chronological order, then the simplicity of the display system is maintained, but the user experience deteriorates due to inability to prioritize relevant comments
Solution Approach 1:
The patent transforms the comment display system from a simple chronological ordering to a scored ranking system by introducing multiple parameters: author reputation score, content quality score, audience interaction score, and comment recency. Each comment is evaluated against these parameters and assigned a composite score that determines its display position. This parameter-based approach maintains operational simplicity (users still see a ordered list) while dramatically improving user experience through intelligent prioritization of relevant comments.
3Measurement precision
If the system calculates comprehensive scores for all comments using multiple signals, then the accuracy of comment prioritization is improved, but the processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary action by pre-calculating and storing author-related signals (reputation scores, interaction history) and content-related signals (grammar quality, originality metrics) before the actual comment ranking occurs. When comments need to be ranked, the system retrieves these pre-computed signals and combines them with real-time audience interaction data, significantly reducing the computational time required for the final scoring process while maintaining high accuracy.
Data Source
AI summary
In one embodiment, a method includes retrieving a set of comments associated with one or more content objects in a social-networking system, determining a score for each of the comments for a selected one of the content objects, where the score for each of the comments is based on signals associated with the comment, and the signals are related to a history of user interactions by a target user with the comments in the set, ordering the comments for the selected content object based on the respective scores, and presenting the ordered comments to the target user. The content objects may be presented in a newsfeed for the target user. The signals may be based on a history of user interactions by other users with the comments in the set, where the other users are connected to the target user in the social-networking system.