Profile-Based Media Modification for Higher User Engagement
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Solution Overview
Problem
Existing media selection systems fail to attract user attention and engagement due to the lack of personalization, as they often present the same media to multiple users, leading to decreased interaction.
Innovation Solution
A processing system that modifies media content based on user profiles by adding, removing, or modifying media assets to increase similarity with user data such as locations, acquaintances, and personal information, using likeness scores and trained models to enhance engagement.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the same media is output to multiple users from pre-generated content, then the server operation is simple and efficient, but the user engagement and attention are decreased
Solution Approach 1:
The system performs preliminary actions by collecting user data from multiple sources (social media profiles, browsing history, purchase history) in advance and storing it in user profiles. This pre-processing of user information enables rapid personalized media selection without real-time data collection delays, thus maintaining efficiency while improving personalization.
Solution Approach 2:
The system changes parameters by modifying existing media content through various transformations including overlaying graphics, adding text, filtering video feeds, and adjusting images. These parameter changes allow the same base media to be adapted into multiple personalized versions for different users, resolving the contradiction between using pre-generated content and providing personalized experiences.
2Adaptability or versatility
If user data is collected and processed to select personalized media, then user engagement increases, but the system complexity and data processing requirements increase
Solution Approach 1:
The system segments the complex task of personalized media selection into distinct modular components: user data collection from multiple sources, user profile storage and management, media selection based on profile matching, and media content modification. This segmentation allows each component to be independently optimized and managed, reducing overall system complexity while maintaining personalization capabilities.
Solution Approach 2:
The system creates simplified copies of user profiles that capture essential personalization attributes without storing or processing all raw user data. These profile copies enable rapid media selection and modification decisions without requiring complex real-time analysis of complete user datasets, thus reducing computational complexity.
3Adaptability or versatility
If media content is modified based on user profiles, then media relevance to users increases, but the processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-collecting and storing user profile data from multiple sources in advance. This pre-processing eliminates the need for real-time data collection during media delivery, allowing rapid profile-based media selection and modification without time-consuming data gathering operations.
Solution Approach 2:
The system applies local quality by making targeted, localized modifications to media content rather than completely regenerating it. Modifications include overlaying specific graphics, adding relevant text, applying selective filters, or adjusting particular image parameters - these focused local changes achieve personalization with minimal processing time compared to complete media regeneration.
Data Source
AI summary
To improve user engagement with media content, a processing system is configured to modify the media content based on one or more user profiles. To this end, the processing system includes one or more servers first configured to select media content from a database to present to a user. The servers are then configured to determine one or more likeness scores between the selected media content and a user profile associated with the user. Based on the comparison of the likeness scores to the user profile, the servers then modify the media content so as to increase one or more of the determined likeness scores. The servers then provide the modified media content to the user.