Privacy-Aware Social Media Content Recommendation System
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Solution Overview
Problem
Existing social media sharing systems struggle to recommend content for sharing in a social setting while respecting users' privacy settings, as they often infringe on access control settings by revealing private content consumption and interaction history.
Innovation Solution
The system employs a multiple layer affinity threshold technique to recommend content for sharing in a social setting, using a combination of accessible and inaccessible data to create personalized content recommendations without violating access control settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the system uses user content consumption and interaction history to generate personalized content recommendations, then the personalization and relevance of recommendations is improved, but user privacy and access control settings are violated
Solution Approach 1:
The patent introduces an intermediary mechanism that separates the recommendation generation process from direct access to private user data. The system uses publicly available interaction data (likes, shares, comments) as intermediaries to infer user preferences without directly accessing or exposing private content consumption history. This mediator layer allows personalization while maintaining privacy boundaries.
Solution Approach 2:
The patent segments user data into different privacy levels (public interactions vs. private consumption history) and processes them separately. The recommendation system primarily relies on public interaction data that users have already shared, while minimizing or eliminating access to private data. This segmentation allows the system to personalize recommendations using only the data segments that users have made accessible.
2Measurement precision
If the system accesses private user data to improve recommendation accuracy, then the quality of content suggestions is improved, but access control settings are infringed upon
Solution Approach 1:
The patent implements a feedback mechanism where the system continuously monitors which public interactions are most predictive of user preferences and adjusts its recommendation strategy accordingly. By analyzing patterns in public likes, shares, and comments, the system improves recommendation accuracy over time without needing to access private data. The feedback loop operates entirely within the boundaries of accessible information.
Solution Approach 2:
The patent changes the parameters used for recommendation from private consumption metrics to public interaction metrics. Instead of measuring user preferences through private viewing history or download data, the system measures engagement through public likes, shares, comments, and other visible interactions. This parameter change maintains recommendation functionality while ensuring access control compliance.
3Adaptability or versatility
If the system reveals private content consumption history in recommendations, then the personalization is enhanced, but harmful information disclosure occurs
Solution Approach 1:
The patent extracts only the necessary public interaction data needed for recommendation purposes while leaving private consumption history separate and protected. The system takes out and utilizes only the public layer of user engagement (visible likes, shares, comments) and deliberately excludes private consumption data from the recommendation generation process, thereby preventing harmful information disclosure while maintaining useful personalization.
Data Source
AI summary
Systems and methods are provided that facilitate selecting videos to share in a messaging session. A system is provided that includes an accessible data mining component configured to generate a first set of data associated with a messaging session between a user and one or more other user, the first set of data excluding data that is inaccessible to the user and comprising data that is accessible to the user, and an identification component configured to identify a set of media items based on the first set of data. An inaccessible data mining component is further configured to generate a second set of data comprising data that is inaccessible to the user but accessible to at least one of the one or more other users, and a recommendation component configured to recommend a subset of the set of media items to the user based on the second set of data.