Multimodal Content Personalization Using Offline User Group Generation
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Solution Overview
Problem
Existing systems struggle to generate content items that accurately align with user profiles due to inefficiencies in updating text assets and high costs of online inference, leading to generic or irrelevant content.
Innovation Solution
A system utilizing multimodal generative AI models processes user data offline to modify static assets like text, image, and audio, leveraging deep neural networks and user engagement metrics to create personalized content items, reducing online inference costs and enhancing relevance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If online inference is used to personalize content items in real-time, then content relevance to user profiles is improved, but computational costs and processing time increase significantly
Solution Approach 1:
The system performs offline processing to pre-generate personalized content items for different user groups before deployment. User profiles are segmented into groups, and content items are personalized in advance using offline inference, eliminating the need for expensive real-time online inference while maintaining personalization quality.
Solution Approach 2:
The system segments users into distinct user groups based on their profiles and characteristics. By dividing the user base into segments, the system can pre-compute personalized content for each group offline, reducing the computational burden during online deployment while still delivering relevant personalized content to each user segment.
2Measurement precision
If text assets are updated frequently to maintain relevance, then content quality is improved, but processing time and system complexity increase
Solution Approach 1:
The system pre-processes and personalizes content items offline before deployment, including all necessary text asset modifications. This preliminary action ensures content quality is optimized in advance, eliminating the need for frequent real-time updates and reducing processing time during online operations.
3Adaptability or versatility
If personalized content items are generated for each user, then user engagement is improved, but resource consumption and processing complexity increase
Solution Approach 1:
The system segments users into groups based on their profiles and generates personalized content items for each user group rather than for every individual user. This segmentation approach maintains high personalization levels and user engagement while significantly reducing computational resources and system complexity compared to fully individualized personalization.
Data Source
AI summary
Methods, computing systems, and technology for automatically generating media assets and content items are presented. The method can include obtaining a plurality of assets of a content provider, the plurality of assets comprising a text asset, an audio asset, an image asset, and a video asset. Additionally, the method can include obtaining, from a content item database, a first content item of the content provider. Moreover, the method can include determining a plurality of user groups for the first content item. Furthermore, the method can include processing, using a machine-learned model, the plurality of assets, the first content item, and a first user group from the plurality of user groups to generate the new content item, wherein the new content item is tailored to the first user group. Subsequently, the method can include storing the new content item in the content item database.