Social Network Content Ranking by Interaction Scores
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Solution Overview
Problem
Users in social networking systems face difficulty in identifying relevant content items from multiple groups they are part of, leading to decreased interaction and engagement, as they receive a large number of content items making it hard to find interesting or relevant content.
Innovation Solution
The social networking system identifies candidate content items that have not been viewed by the user, computes interaction scores based on user interactions such as preferences, shares, and comments, and presents them to the user, with higher scored items being prioritized, to increase visibility and engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If users are included in multiple groups receiving content items, then the quantity of content items increases, but the ability to identify relevant content decreases
Solution Approach 1:
The system changes the parameter of content selection from random or chronological to interaction-score-based ranking. By computing interaction scores that quantify user engagement levels and using these scores to rank content items, the system transforms the overwhelming quantity of content into a manageable ranked list where relevant content rises to the top, directly resolving the contradiction between content quantity and identifiability.
2Quantity of substance
If all content items are presented to users, then completeness of content delivery increases, but user engagement decreases due to information overload
Solution Approach 1:
The system extracts and prioritizes high-value content items based on interaction scores rather than presenting all content equally. By computing interaction scores for each content item and selectively presenting those with higher scores first (or exclusively), the system extracts the most engaging content from the complete set, maintaining completeness in principle while delivering only the most relevant items in practice, thus preserving user engagement.
3Measurement precision
If content items are ranked by interaction scores, then relevance of presented content increases, but system complexity increases
Solution Approach 1:
The system implements self-service through automated interaction score computation and content ranking. Rather than requiring manual curation or complex administrative intervention, the system automatically computes interaction scores based on user behaviors (views, likes, shares, comments) and uses these scores to rank and present content. This automated self-service approach achieves high relevance precision while keeping operational complexity manageable.
Data Source
AI summary
A social networking system maintains various groups that each include one or more users and maintains information describing interactions by users with the social networking system. The social networking system selects various content items provided to groups including the user based on amounts of interaction with content items provided to groups including the user by other social networking system users as well as amounts of time since the social networking system obtained various content items provided to the groups. Information describing the selected content items is presented to the user by the social networking system outside of pages of content maintained by the social networking system for groups to which the selected content items were provided.