Personalized Video Generation Using Segmented Template Engines
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Solution Overview
Problem
Current social networking systems lack the ability to automatically generate personalized videos using catalogs of items, failing to effectively showcase relevant products to users based on their interests and interactions.
Innovation Solution
A video creation system that uses a ranking engine to identify relevant items, a grouping engine to categorize similar items, and a video generation engine to create personalized videos with a unified narrative, incorporating features like call-to-action buttons, transitions, and logos, which can be updated for item changes such as availability or pricing.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If personalized videos are generated for each user using catalog items, then user engagement and relevance are improved, but system complexity and resource consumption increase significantly
Solution Approach 1:
The system segments the video generation process into distinct modular components: a template engine that handles video structure and narrative, a ranking engine that selects relevant items, and a composition engine that assembles final videos. This segmentation allows each component to be independently optimized and managed, reducing overall system complexity while maintaining personalization capability.
Solution Approach 2:
The system performs preliminary actions by pre-defining video templates with fixed narratives, transitions, and structural elements. These templates are created in advance and stored for reuse, eliminating the need to generate video structures from scratch for each user. The ranking engine also pre-ranks catalog items based on user profiles, preparing personalized content lists before actual video generation occurs.
2Loss of information
If videos are generated dynamically based on user interests and item catalogs, then content relevance is improved, but generation time and processing resources increase
Solution Approach 1:
The system creates and reuses video templates that serve as reusable patterns for video generation. Instead of creating unique video structures for each user, the system copies and customizes pre-defined templates by inserting relevant items from the catalog. This copying approach dramatically reduces generation time while maintaining content relevance through item substitution within the fixed template structure.
Solution Approach 2:
The system changes parameters such as item selection, user profile attributes, and ranking criteria to personalize videos without altering the fundamental video structure. By modifying only the content parameters (which items to include) rather than structural parameters (video layout, transitions, narrative flow), the system achieves personalization efficiently with minimal generation time.
3Quantity of substance
If the system showcases multiple similar items in personalized videos, then product visibility and sales opportunities increase, but video complexity and information overload increase
Solution Approach 1:
The system applies local quality by displaying multiple similar items (e.g., different colors or styles of the same product) in specific designated locations within the video template. Rather than randomly distributing items throughout the video, the template defines specific slots and positions where similar items appear together, creating localized product showcases that are visually organized and easy to process for viewers.
4Reliability
If videos are updated dynamically when item availability or pricing changes, then content freshness and accuracy are improved, but processing overhead and system load increase
Solution Approach 1:
The system implements feedback mechanisms where the ranking engine continuously monitors item catalog changes (availability, pricing, new arrivals) and user interaction data. When changes are detected, the feedback loop triggers selective video regeneration only for affected user segments or item categories, rather than updating all videos system-wide. This feedback-driven approach maintains content accuracy while minimizing unnecessary processing overhead.
Data Source
AI summary
In one embodiment, a method includes identifying a particular item of interest of a user; determining a grouping of a number of items that are similar to the particular item; automatically creating a new video by combining the particular item and the respective number of grouped items that are similar to the particular item in accordance with a video-creation template; and providing the created video for display on a client device of the user.