Content Ranking Using Network Effect Scores
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Solution Overview
Problem
Social networks face challenges in optimizing the presentation of content items to users, often neglecting overall engagement which can lead to reduced participation and health of the online service.
Innovation Solution
A system and method that utilize processors to receive and analyze user engagement data, generate a model characterizing network effects, calculate predicted network effect scores, and organize content items for presentation, incorporating features like interaction graphs, engagement scoring, and regression analysis to enhance user interaction.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If content items are ranked based on individual user satisfaction metrics, then user-specific content quality is improved, but overall network engagement deteriorates
Solution Approach 1:
The patent segments the engagement metric into two distinct components: individual user satisfaction score and network effect score. The ranking system separately calculates these two metrics and then combines them, allowing optimization for both user-specific quality and overall network engagement without one compromising the other
Solution Approach 2:
The patent merges two previously separate ranking objectives (user satisfaction and network engagement) into a unified ranking system. By combining the user satisfaction score with the network effect score in a weighted formula, the system achieves both individual user content quality and overall network engagement simultaneously
2Ease of operation
If traditional ranking methods are used that focus on individual user experience, then content relevance to user is improved, but network-wide participation deteriorates
Solution Approach 1:
The patent implements a feedback mechanism where the network effect score, derived from aggregate user interactions and engagement patterns, feeds back into the ranking system. This feedback loop ensures that content promoting network-wide participation is identified and prioritized, while still maintaining relevance to individual users through the satisfaction score component
Data Source
AI summary
A system comprising a processor and a memory storing instructions that, when executed, cause the system to receive a record of data describing user engagement with content items in an online service; prepare the record of data for generating a model characterizing a network effect of a user interaction with a content item in the online service; generate the model characterizing the network effect of the user interaction with the content item in the online service; generate a predicted network effect score for a plurality of content items based on the model; organize the plurality of content items based on the predicted network effect score; and transmit the plurality of organized content items for presentation to a user. The disclosure also includes similar methods and computer program products.