Video Group Refinement Interface Using Seed Selection
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Solution Overview
Problem
Existing data processing systems for online video platforms face challenges in efficiently identifying and selecting video groups with specific features, such as mood, aesthetic, or topicality, from a vast number of video channels, leading to high computational and network bandwidth burdens.
Innovation Solution
The system employs seed video groups and refinement techniques using positive and negative keywords to identify and select video groups with particular features, reducing the need to browse through numerous videos or channels, and optimizing resource usage by accurately packaging content aligned with user interests.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system browses through numerous video channels to identify video groups with specific features, then the accuracy of selecting video groups with desired features (mood, aesthetic, topicality) is improved, but the computational and network bandwidth burden increases significantly
Solution Approach 1:
The system pre-computes and stores feature embeddings for video groups during off-peak times, including mood, aesthetic, and topicality features. This preliminary action allows the system to quickly query and retrieve matching video groups without performing intensive computations during user requests, thereby resolving the contradiction between selection accuracy and computational burden
Solution Approach 2:
The system replaces traditional mechanical browsing and manual video group selection with automated machine learning models that analyze video content and generate feature embeddings. This substitution enables accurate identification of video groups with specific features while significantly reducing the computational and network resources required compared to manual or rule-based approaches
2Speed
If the system distributes video groups to users without refined selection, then the speed of content delivery is improved, but the distribution of non-relevant content consumes unnecessary network resources
Solution Approach 1:
The system implements feedback mechanisms where user interactions with video groups (views, likes, shares) are continuously monitored and used to refine the recommendation algorithm. This feedback loop ensures that video groups are accurately matched to user preferences, preventing the distribution of non-relevant content while maintaining efficient content delivery speed
Solution Approach 2:
The system dynamically adjusts recommendation parameters such as mood, aesthetic, and topicality weights based on user behavior patterns and contextual information. By changing these parameters in real-time, the system optimizes the relevance of distributed video groups, ensuring network resources are not wasted on non-relevant content while maintaining fast delivery speeds
Data Source
AI summary
This disclosure relates to digital video analysis. In one aspect, a method includes providing a user interface that enables a user of the computing system to select one or more seed video groups and one or more keywords, wherein each seed video group comprises one or more videos. The user interface is updated to provide candidate video groups selected based on the one or more seed video groups and the one or more keywords and, for each candidate video group, a first user interface control that enables the user to refine the set of candidate video groups to include video groups classified as being similar to the candidate video group. Data indicating user interaction with a given first user interface control for a first candidate video group is received. The user interface is updated to provide an updated set of candidate video groups.