Content Access Prediction for Virtual Communities
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Users face challenges in sharing content across virtual communities as they are unaware whether recipients have access to the various layers of content, leading to potential access denial and significant effort in requesting access permissions.
Innovation Solution
A method and system that predicts recipient access likelihoods and submits access requests to content owners, determining recipient access and prediction scores based on social graphs, public information, and content interests to streamline content sharing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If users manually request access permissions for each recipient, then access control accuracy is improved, but time consumption and effort increase significantly
Solution Approach 1:
The system performs preliminary actions by automatically determining recipient access likelihoods and prediction scores before content sharing occurs. It pre-evaluates multiple content layers and recipients, then submits access requests in advance, eliminating the need for manual permission requests after content is shared.
Solution Approach 2:
The system enables self-service by automatically determining access likelihoods, prediction scores, and submitting access requests without requiring user intervention. The system serves itself by processing content sharing operations autonomously based on predicted access patterns.
2Reliability
If users manually request access permissions for each recipient, then access control accuracy is improved, but operational complexity increases
Solution Approach 1:
The system performs preliminary actions by automatically determining recipient access likelihoods and prediction scores before content sharing occurs. It pre-evaluates multiple content layers and recipients, then submits access requests in advance, eliminating the need for manual permission requests after content is shared.
Solution Approach 2:
The system enables self-service by automatically determining access likelihoods, prediction scores, and submitting access requests without requiring user intervention. The system serves itself by processing content sharing operations autonomously based on predicted access patterns.
3Loss of time
If the system predicts access likelihoods and automates access requests, then time and effort are reduced, but measurement precision of access prediction is required
Solution Approach 1:
The system uses feedback mechanisms where content owners receive predicted access likelihoods and can adjust access decisions accordingly. The system continuously learns from actual access patterns and refines its prediction models, improving measurement precision over time while maintaining automation benefits.
Data Source
AI summary
Some users (also called sharers) desire to share content with recipients (also called recipients) over a virtual community or other computerized means. Some of the content to be shared (first-order content) may include links, hyperlinks, or other references (links) to additional content (each individually a second-order content). These content layers may continue indefinitely and the content layers may contain links both within and without the domain or virtual community of the first-order content. A sharer may be unaware of whether various recipients have access to any of the various layers of content (first-order or otherwise). Embodiments of the present invention transmit the likelihood for individual recipients to access various layers of content to the sharer.


