Video Group Selection via Co-Interaction and Topicality Scoring
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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 uses seed video groups and keywords to calculate co-interaction and topicality scores, allowing for the identification and selection of video groups with particular features, such as co-interaction patterns and topicality, using collaborative filtering and annotation analysis, thereby reducing the need to browse through numerous individual videos or channels.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If the system browses through numerous individual videos or channels to identify video groups with specific features, then the accuracy of selecting relevant video groups is improved, but the computational and network bandwidth burdens increase significantly
Solution Approach 1:
The system pre-calculates and stores co-interaction scores and topicality scores for video groups before user requests. These scores are computed based on collaborative filtering of user interaction data and annotation analysis, allowing the system to quickly retrieve and combine pre-computed results rather than performing intensive calculations in real-time when users search for video groups
Solution Approach 2:
The patent introduces intermediate scoring mechanisms (co-interaction scores and topicality scores) that act as mediators between raw user interaction data and final video group recommendations. These intermediate scores condense complex user behavior patterns and content annotations into manageable metrics that can be efficiently combined and queried, reducing the computational burden of direct video-by-video analysis
2Adaptability or versatility
If the system transmits all candidate video groups to users for browsing, then users can find relevant content, but network bandwidth is wasted transmitting non-relevant content
Solution Approach 1:
The system applies different quality thresholds and scoring weights to different video group candidates based on their co-interaction scores and topicality scores. Video groups with higher scores receive preferential treatment in terms of transmission priority and visibility, while lower-scoring candidates are filtered out or deprioritized, ensuring that network resources are allocated efficiently to the most relevant content
Solution Approach 2:
The patent dynamically adjusts the combination of co-interaction score and topicality score to generate a final relevance ranking. By changing the parameters and weights of these scores based on user preferences, context, and performance metrics, the system optimizes which video groups are transmitted to users, balancing relevance accuracy with network efficiency
Data Source
AI summary
This disclosure relates to digital video analysis. In one aspect, a method includes receiving data indicating one or more seed video groups that each include one or more seed videos. Data indicating one or more keywords is received. A set of candidate video groups that each include one or more candidate videos is identifier. For each candidate video group in the set of candidate video groups a co-interaction score and a topicality score are determined. A subset of the candidate videos groups is selected based on the co-interaction score and the topicality score of each candidate video group. Data indicating the subset of candidate video groups is provided for presentation.