Content Scan Version Selection for Rendering Success
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Conventional content distribution methods in social networking systems face challenges with long load times and interrupted interactions due to the large size of multimedia content items, leading to a poor user experience when scrolling through content feeds.
Innovation Solution
A system that calculates render scores for multiple scan versions of content items using a machine learning model, selecting the highest quality scan version that meets a render score threshold for transmission to ensure successful rendering on client devices, thereby improving content delivery and user experience.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If high-quality content items are transmitted to client devices, then content quality is improved, but load time increases and user interaction is interrupted
Solution Approach 1:
The content item is divided into multiple scan versions with different quality levels and file sizes. The system segments the content into versions that can be selectively transmitted based on device capabilities and network conditions, allowing quality optimization without always transmitting the full high-quality version.
Solution Approach 2:
The system changes the parameter of content quality by selecting different scan versions based on rendered quality scores. Instead of always transmitting the highest quality version, the system dynamically adjusts the quality parameter to match what is actually needed for successful rendering on the specific client device.
2Reliability
If multiple scan versions of content items are transmitted, then rendering success rate is improved, but data transmission volume increases
Solution Approach 1:
The system performs preliminary calculation of rendered quality scores for multiple scan versions before transmission. By pre-evaluating which scan versions are most likely to render successfully on the specific client device, the system can select and transmit only the necessary versions, avoiding unnecessary data transmission while ensuring rendering success.
Solution Approach 2:
The system uses feedback from the machine learning model's rendered quality score calculations to determine which scan versions to transmit. The feedback mechanism identifies the optimal subset of scan versions based on device characteristics and content properties, ensuring high rendering success rates without transmitting all possible versions.
3Device complexity
If conventional content distribution methods are used, then system complexity is low, but user experience deteriorates due to long load times and interrupted interactions
Solution Approach 1:
The system introduces an intermediary machine learning model that calculates rendered quality scores to bridge the gap between content quality and device capabilities. This intermediary component automatically makes intelligent decisions about which scan versions to transmit, improving user experience without requiring complex manual configuration or high system overhead.
Data Source
AI summary
Systems, methods, and non-transitory computer-readable media can, for a first content item comprising a plurality of scan versions, calculate render scores for at least some of the plurality of scan versions, each render score being indicative of a likelihood of an associated scan version to render successfully on a first client computing device. A first scan version of the plurality of scan versions is selected based on the render scores. The first scan version of the first content item is transmitted to the first client computing device.