Content Feed Composition Modulation for User Engagement
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Solution Overview
Problem
Content feeds, such as those in social networks, often suffer from user fatigue due to low-quality, repetitive, or irrelevant content, leading to decreased long-term engagement as users become disinterested.
Innovation Solution
A system that analyzes user behavior and interaction data to identify factors affecting engagement, modulates content feed composition by adjusting the proportion of different update types, and verifies the effectiveness of these changes to improve long-term user interaction with the content feed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If content feeds present more content items to users, then user engagement increases, but user fatigue and disinterest increase due to low-quality or repetitive content
Solution Approach 1:
The patent applies local quality by customizing the content feed composition for different user segments based on their specific characteristics, behaviors, and preferences. Instead of presenting uniform content to all users, the system tailors the proportion and types of content items (e.g., news, updates, promotions) to each user segment, thereby maintaining high engagement while avoiding user fatigue from irrelevant or repetitive content.
Solution Approach 2:
The patent implements dynamics by continuously adjusting the feed composition based on real-time user feedback, engagement metrics, and changing user preferences. The system dynamically modifies the proportion of different content types in user feeds, allowing it to adapt to evolving user interests and prevent fatigue while maintaining engagement over time.
2Productivity
If content feed composition is optimized for immediate engagement, then short-term interaction increases, but long-term engagement decreases due to user fatigue
Solution Approach 1:
The patent applies preliminary action by proactively managing content feed composition to prevent user fatigue before it occurs. The system uses historical engagement data and user preferences to pre-adjust the mix of content types, ensuring that users are exposed to diverse, high-quality content that maintains interest over time rather than maximizing immediate engagement at the cost of long-term retention.
Solution Approach 2:
The patent implements feedback mechanisms that continuously monitor user engagement metrics and feed back into the content selection algorithm. This closed-loop system allows the platform to detect early signs of user fatigue and adjust feed composition accordingly, balancing immediate engagement goals with long-term user retention by learning from actual user responses to different content types.
3Adaptability or versatility
If diverse content types are presented in the feed, then user interest is maintained, but feed complexity and difficulty of optimization increase
Solution Approach 1:
The patent applies segmentation by dividing the user base into distinct segments based on shared characteristics, behaviors, and preferences. For each segment, the system defines specific content composition rules and proportions, simplifying the optimization process by handling diverse user groups separately rather than trying to optimize for all users simultaneously with a single complex algorithm.
Solution Approach 2:
The patent uses parameter changes by adjusting the proportions and weights of different content types in the feed based on user segment characteristics and engagement metrics. Rather than fundamentally changing the feed structure, the system optimizes by modifying parameters such as content mix ratios, prioritization weights, and selection thresholds, which simplifies the complexity of managing diverse content types.
Data Source
AI summary
The disclosed embodiments provide a system for improving long-term engagement with content feeds. During operation, the system obtains a factor associated with a change in a level of engagement with a content feed. Next, the system uses the factor to modulate a feed composition of the content feed for a first set of users. The system then verifies an effect of the factor on the level of engagement by comparing a first level of engagement of the first set of the users with the content feed with a second level of engagement of a second set of users with the content feed. Finally, the system uses the first and second levels of engagement with the content feed to select a value associated with the factor for use in modulating a subsequent feed composition of the content feed.