Contextual Media Tagging for Selective Exposure
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Solution Overview
Problem
Users of social networking and media sharing sites must manually specify recipients for shared media files, which is inefficient and requires additional steps, as existing systems lack the ability to automatically share media based on contextual information.
Innovation Solution
A computer-implemented system that generates contextual information for users and automatically shares media files with individuals in their social network based on this information, using a database to store media files and contextual data, and applying semantic graphs to identify appropriate recipients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If users manually specify recipients for each media file, then sharing accuracy is improved, but user effort and time consumption increase
Solution Approach 1:
The system pre-generates contextual information about users (interests, activities, demographics) before media sharing occurs. This preliminary data preparation enables automatic recipient identification without requiring users to manually specify recipients each time, thus reducing user effort while maintaining sharing accuracy through context-based matching.
2Ease of operation
If automatic sharing based on context is implemented, then user effort is reduced, but sharing precision may deteriorate
Solution Approach 1:
The system uses contextual information (user interests, activities, demographics) as feedback mechanisms to automatically identify appropriate recipients. The contextual data serves as a feedback loop that continuously refines recipient selection accuracy without requiring manual user input, thus maintaining sharing precision while reducing user effort.
3Extent of automation
If contextual information processing is added, then automation level increases, but system complexity increases
Solution Approach 1:
The system employs a multi-functional contextual information processing module that handles multiple tasks: generating user profiles, analyzing media content, identifying recipients, and managing sharing rules. This universal module consolidates multiple functions into a single system component, increasing automation level while managing system complexity through functional integration rather than proliferation of separate components.
Data Source
AI summary
A computer-implemented system and method for providing contextual media tagging for selective media exposure is provided. A media file is maintained in a database. Contextual information is generated for a user. The media file is associated with the user contextual information, and the media file is shared to individuals in a social network of the user based on the user contextual information.