Social Network Feed Transparency for Relevance Filtering
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Solution Overview
Problem
Users of social networking systems face challenges in managing large volumes of information feed items, where many are irrelevant, leading to time-consuming efforts to identify and synthesize relevant posts, comments, and updates.
Innovation Solution
The system displays feed items at different transparency levels based on affinity scores calculated from user interactions, allowing more relevant items to be highlighted by adjusting opacity levels in the user interface.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If the information feed displays all feed items uniformly, then the user can access complete information, but the user spends excessive time identifying relevant items
Solution Approach 1:
The patent applies local quality by displaying different feed items at different transparency levels based on their relevance to the user. Relevant items are displayed at higher transparency (more opaque) while less relevant items are displayed at lower transparency, allowing users to quickly identify and focus on important information without losing visibility of any feed items.
Solution Approach 2:
The patent uses transparency changes (similar to color changes in TRIZ) to visually distinguish feed items by relevance. By adjusting the opacity/transparency level of each feed item based on affinity scores, the system creates visual differentiation that helps users rapidly identify relevant content without changing the fundamental structure or layout of the feed.
2Productivity
If the system calculates affinity scores for all information sources, then feed items can be effectively prioritized, but the computational complexity increases
Solution Approach 1:
The system performs preliminary action by pre-calculating and storing affinity scores between users and information sources before the user views the feed. These pre-computed affinity scores are then used to automatically determine transparency levels for feed items, eliminating the need for real-time complex calculations and reducing computational burden during feed rendering.
Solution Approach 2:
The system uses self-service by leveraging existing user interaction data and preferences to automatically generate affinity scores without requiring additional user input or configuration. The system serves itself by utilizing already-collected user behavior data to autonomously prioritize feed items, reducing the need for manual intervention or complex user-facing configuration.
Data Source
AI summary
A method of providing feed information is provided. The method comprises calculating an affinity score for each information source that provides updates to an information feed for a first user of a social networking system wherein the affinity score for each information source is calculated based on interaction factors between the first user and the information source. The method further comprises assigning a transparency value to each information source by converting the affinity score to a transparency value, generating the information feed of the social networking system for the first user wherein the information feed includes updates from the information sources, sending the information feed and the transparency value for each information source to a web browser operated by the first user, and instructing the web browser to display each update at a transparency level that corresponds to the transparency value assigned to the information source that provided the update.