Comment Ranking and Filtering Based on Author Signals
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Solution Overview
Problem
Social networking platforms face challenges in effectively ranking and filtering user comments on content items, as existing methods fail to accurately identify and prioritize comments that are most relevant and engaging to users, leading to a suboptimal user experience.
Innovation Solution
A method that assigns scores to comments based on various signal values related to author reputation, content quality, audience interactions, and historical user impressions, using a combination of signal values to rank and filter comments, ensuring that high-scoring comments are displayed prominently and relevant content is prioritized.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple signal values are used to rank comments, then comment relevance and engagement quality improve, but system complexity increases
Solution Approach 1:
The patent segments the comment ranking problem into multiple independent signal components (author reputation signal, content quality signal, audience interaction signal, historical impression signal). Each signal is calculated separately based on specific features, then combined to produce the final ranking score. This segmentation allows the system to maintain high measurement precision through multiple signals while managing complexity by treating each signal as an independent module.
Solution Approach 2:
The patent creates a universal ranking framework that uses multiple signals serving different functions (author credibility assessment, content quality evaluation, audience engagement measurement, historical performance tracking). This multi-functional approach allows a single ranking system to handle diverse comment attributes simultaneously, improving overall relevance accuracy without requiring separate ranking systems for each attribute.
2Reliability
If comprehensive signal values are calculated for each comment, then user experience quality improves, but processing time increases
Solution Approach 1:
The patent calculates various signal values (author reputation, content quality, audience interaction, historical impressions) in advance and stores them in the social-networking system. When comments need to be ranked, the system retrieves these pre-calculated signals rather than computing them from scratch, significantly reducing processing time while maintaining comprehensive signal analysis for high user experience quality.
3Productivity
If comments are ranked and filtered based on multiple criteria, then engagement quality improves, but computational resources increase
Solution Approach 1:
The patent implements filtering based on threshold values for different signal parameters. Comments are evaluated against multiple criteria (author reputation threshold, content quality threshold, audience interaction threshold), and only comments meeting the thresholds are prominently displayed. This parameter-based filtering approach maintains high engagement quality by selecting only the best comments while reducing computational resource usage by eliminating low-quality comments early in the ranking process.
Data Source
AI summary
In one embodiment, a method includes retrieving a plurality of comments associated with a content object on a social-networking system, determining a score for each of the comments, wherein the score is based on one or more signals associated with the comment, and where the signals are related to the identity of an author of the comment or content of the comment, ordering the comments based on the respective scores, and presenting one or more of the ordered comments to a target user. The method may further include excluding one or more of the comments based on a filtering condition. One of the signals may be based on how many times the comment has been (a) liked, (b) hidden, (c) marked as spam, or (d) replied to within a specified period of time. One of the signals may be based on a reputation of the author of the comment.