Live Video Topic Segmentation for Interest Matching
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for recommending live videos to users fail to consider that users may be interested in specific portions of a video but not the entire video, leading to ineffective recommendations during streaming, as they focus on video popularity rather than content relevance.
Innovation Solution
An online system dynamically identifies topics within live videos based on image frames during streaming and matches these topics with user interests using machine learning techniques to rank and recommend videos that are likely to engage users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If current methods evaluate video interest based on overall video popularity, then recommendation simplicity is maintained, but recommendation accuracy for specific user interests deteriorates
Solution Approach 1:
The patent segments a live video into multiple time segments (e.g., 5-minute intervals) and evaluates user interest for each segment separately using image frames from that segment. This allows the system to identify which specific portions of a video match user interests, rather than treating the entire video as a single unit. The segmentation enables precise matching of user interests with relevant video portions while maintaining manageable processing complexity.
Solution Approach 2:
The system performs preliminary analysis of live video content by extracting and analyzing image frames at regular intervals during streaming. This preliminary action of segmenting and pre-evaluating video content allows the system to be ready with interest match information when users request recommendations, improving response time and accuracy without requiring complex real-time analysis at the moment of recommendation.
2Measurement precision
If live video content is analyzed in real-time during streaming, then content relevance to user interests is improved, but processing time and computational resources increase
Solution Approach 1:
The system employs periodic action by analyzing live video content at fixed time intervals (e.g., every 5 minutes) rather than continuously. Image frames are extracted and analyzed at these periodic points during the live stream. This approach maintains content relevance accuracy by regularly updating the analysis while avoiding the excessive processing burden of continuous real-time analysis, thus balancing precision with processing time efficiency.
3Ease of operation
If video recommendations are made based on overall popularity, then system simplicity is maintained, but user engagement with relevant content decreases
Solution Approach 1:
The patent applies local quality by matching specific portions of live video content (identified through periodic image frame analysis) with specific user interests. Instead of treating all videos uniformly based on overall popularity, the system identifies which local segments of videos align with individual user profiles and interests. This enables personalized recommendations that improve user engagement while maintaining operational simplicity through automated interest matching algorithms.
Data Source
AI summary
An online system is configured to recommend a live video to a target user by presenting the live video in the target user's newsfeed or other location. The online system receives a plurality of live videos for streaming to its users. To recommend a live video, the online system dynamically identifies a topic of each of the plurality of live videos during the streaming. The online system further selects a group of candidate live videos, each of which has an identified topic matching an interest of the target user. The online system ranks the candidates based on a probability that the target user would positively interact with each candidate live video if the candidate live video is presented to the target user. Then the online system provides a candidate above a threshold level in the ranking for display to the target user, e.g., in a newsfeed of the target user.