Endorsement Interface Merging Social and Source Correlations
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Solution Overview
Problem
Users face inefficiency in transitioning between interfaces or windows to perform actions like endorsing content, as they often have multiple windows open while engaging with different types of online content, making it cumbersome to interact with recommended content effectively.
Innovation Solution
A system that processes user inputs to determine social and source correlations, generating graphical data for a user interface element that provides recommended content based on endorsement actions, allowing for seamless interaction with endorsed items within a share box, including action buttons for commenting, sharing, or transitioning to view the content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If users have multiple windows open while viewing different content, then users can access diverse content sources, but it becomes cumbersome and inefficient to transition between interfaces to endorse content
Solution Approach 1:
The patent merges the endorsement interface with the content viewing interface by integrating the endorsement button directly into the webpage display. This allows users to endorse content without leaving the current window or transitioning to a separate interface, thus maintaining content access diversity while improving operational efficiency.
Solution Approach 2:
The endorsement button is designed to serve multiple functions: it can be triggered by various user inputs (cursor hover, selection, sharing actions) and provides unified endorsement functionality across different content types and sources within the same interface, eliminating the need for separate endorsement windows.
2Reliability
If users must transition between different interfaces or windows to endorse content, then content endorsement functionality is available, but user engagement and interaction efficiency are reduced
Solution Approach 1:
The endorsement button is pre-positioned on the webpage interface, ready to be activated by various user inputs before the user needs to perform the endorsement action. This preliminary preparation of the interface eliminates the need for window transitions and maintains continuous user engagement.
Solution Approach 2:
The system automatically detects and responds to various user inputs (cursor hover, selection, sharing actions) to trigger the endorsement button without requiring users to manually navigate between interfaces. This self-service mechanism maintains endorsement functionality while maximizing user engagement efficiency.
3Measurement precision
If the system provides recommended content based on social and source correlations, then content relevance is improved, but the complexity of processing and determining correlations increases
Solution Approach 1:
The system uses social graph data and source correlation data as intermediaries to bridge the gap between user interactions and content recommendations. These pre-computed correlation datasets simplify the processing complexity while maintaining high relevance accuracy by pre-establishing relationships between users, content, and sources.
Solution Approach 2:
The system pre-processes and stores social correlations and source correlations in datasets before they are needed for content recommendation. This preliminary computation of correlations reduces the real-time processing complexity while maintaining measurement precision for content relevance.
Data Source
AI summary
In one aspect, a system for providing a user interface including recommended content in response to an endorsement input is described. The system includes a processor and a memory storing instructions that, when executed, causes the system to: receive an input from a first user; determine that the input is related to an endorsement of a first content item from a first source; determine a social correlation between the first content item and a second content item from a second source, determine a source correlation between the first source and the second source, determine recommended content using the social correlation and the source correlation and provide the recommended content to the first user.