Social Content Display Scoring System
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Solution Overview
Problem
Existing social content providers fail to effectively display social content in a manner that aligns with user preferences, as they do not adequately consider user interactions and relationships with different social content providers when presenting related online content.
Innovation Solution
The method involves identifying social content related to online content, determining the user's interaction with social content providers, calculating numerical scores based on these interactions, and displaying social content ordered by these scores, thereby prioritizing content from providers inferred to be preferred by the user.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If social content is displayed without considering user interactions, then the display system is simple, but the relevance to user preferences deteriorates
Solution Approach 1:
The system pre-calculates scores for social content providers based on user interactions (likes, shares, comments, time spent) before content needs to be displayed. These scores are stored and updated over time, so when content needs to be displayed, the system can quickly retrieve and use the pre-computed scores to rank content, avoiding complex real-time analysis while maintaining high relevance to user preferences
Solution Approach 2:
The system continuously monitors user interactions with social content and uses this feedback to update the scores of social content providers. When users engage with content from specific providers (liking, sharing, commenting, or spending time viewing), the system adjusts the scores accordingly, creating a dynamic feedback loop that improves relevance over time while maintaining a manageable system structure
2Ease of operation
If social content from all providers is displayed equally, then the system is easy to operate, but the user experience deteriorates
Solution Approach 1:
Instead of applying a uniform display approach to all social content providers, the system assigns different weights and priorities to different providers based on their individual scores. Each provider's content is displayed with a level of prominence proportional to how well that provider has performed in meeting user preferences, creating locally optimized display quality for each provider while maintaining overall system simplicity
3Measurement precision
If user interactions with multiple social content providers are tracked, then content relevance improves, but data processing complexity increases
Solution Approach 1:
The system extracts and isolates specific interaction metrics (likes, shares, comments, time spent) from the complex stream of user data, focusing only on the most relevant signals for determining content preference. By extracting and weighting only these key interaction types, the system achieves accurate content relevance without needing to process and analyze every possible data point, thereby reducing overall data processing complexity while maintaining high measurement precision
Data Source
AI summary
In general, one aspect of the subject matter described in this specification can be embodied in methods that include identifying social content related to online content for display to a user, determining social content providers associated with the identified social content, calculating quantities based on the user's interaction with each of the respective social content providers, and displaying at least some of the social content based on the calculated quantities. Other embodiments of this aspect include corresponding systems, apparatus, and computer program products.