Dynamic Video Content Contextualization via Social Influence
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Solution Overview
Problem
Current contextual computing technologies lack the ability to dynamically generate personalized video content in real-time based on viewer context, leading to videos being either too generic or not effectively influenced by social influencers, resulting in inadequate audience engagement.
Innovation Solution
A method and system that identify viewers, generate preference profiles, correlate them with social profiles and influencers, compute potential video advertisements from templates, and rank them based on contextual influence, allowing for real-time dynamic contextualization of video content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If video content is made generic to appeal to broad audiences, then audience reach is improved, but audience engagement and personalization deteriorate
Solution Approach 1:
The system dynamically generates video content by combining video templates with real-time sensor data from multiple devices. The video content adapts to individual viewers based on their context, preferences, and social influencer connections, transforming static generic videos into dynamic personalized experiences that maintain broad reach while enhancing engagement.
Solution Approach 2:
The system segments the audience into individual viewers with unique preference profiles and social connections. By analyzing sensor data from multiple devices and correlating it with social profiles, the system creates personalized video content for each viewer while maintaining the ability to reach broad audiences through template-based generation.
2Productivity
If real-time personalized video content is generated for each viewer, then audience engagement is improved, but system complexity and processing requirements worsen
Solution Approach 1:
The system performs preliminary actions by pre-collecting and analyzing sensor data from multiple devices, generating preference profiles, and identifying social influencers before video content generation. This advance preparation reduces real-time processing complexity while enabling highly personalized video content creation.
Solution Approach 2:
The system introduces intermediary components including preference profile generators, social profile correlators, and contextual influence determiners that mediate between raw sensor data and video content generation. These intermediaries simplify the overall system architecture by breaking down complex processing into manageable stages.
3Loss of information
If social influencer data is integrated into video personalization, then contextual relevance is improved, but data processing requirements worsen
Solution Approach 1:
The system extracts only the essential social influencer data needed for contextualization from extensive social profiles. By identifying and extracting key influencer connections and preferences relevant to each viewer, the system maintains high contextual relevance while reducing the volume of data that requires processing.
Data Source
AI summary
A method includes identifying, by a communication device, an audience including one or more viewers, generating a preference profile for the audience, correlating the generated preference profile to one or more social profiles, determining, based on the generated preference profile, the one or more social profiles, and a database of social influencers, a contextual influence, the contextual influence identifies and quantifies a possible reaction of the audience to a video content, computing a plurality of potential video advertisements from a plurality of video templates, the computing is based on the determination of the contextual influence, and ranking the computed plurality of potential video advertisements according to the contextual influence.