Video Recommendation System Using Segment-Level Feature Analysis
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Solution Overview
Problem
Conventional video recommendation systems cannot analyze user feelings towards specific scenes in videos and thus fail to recommend video segments that are most preferred by users, relying solely on user feedback for broad video evaluations.
Innovation Solution
A video recommendation system that includes an interaction device to receive user feedback, a feature calculating module to analyze object features of video segments, and an analysis module to recommend video segments based on calculated features and feedback information, allowing for personalized recommendations by calculating object features and popularity across multiple users.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional video recommendation systems use user feedback for broad video evaluations, then they can implement simple recommendation algorithms, but they cannot analyze user feelings towards specific scenes in videos
Solution Approach 1:
The patent segments videos into multiple video segments and analyzes user feedback at the segment level rather than for entire videos. The system divides video content into discrete units (segments) and evaluates user preferences for each segment independently, enabling precise scene-level analysis while maintaining manageable computational complexity through structured segmentation.
Solution Approach 2:
The patent introduces an intermediary layer of video segment analysis between raw user feedback and final recommendations. By inserting video segment evaluation as an intermediate step, the system translates coarse user feedback into fine-grained scene preferences, bridging the gap between simple feedback collection and sophisticated scene-level understanding.
2Productivity
If video recommendation systems analyze video content at fine granularity, then they can recommend specific favorite video segments, but they require complex content analysis capabilities
Solution Approach 1:
The system segments video content into manageable units and processes feedback for each segment separately. This segmentation enables high-resolution recommendation precision by analyzing specific video segments independently, while the modular structure keeps the analysis system complexity controlled through systematic division of processing tasks.
Solution Approach 2:
The patent changes the analysis parameter from video-level to segment-level granularity. By adjusting the resolution parameter of video analysis from coarse (whole video) to fine (individual segments), the system achieves higher recommendation precision without proportionally increasing overall system complexity, as the same analysis framework applies recursively to smaller units.
3Adaptability or versatility
If conventional systems perform statistics on videos within a large range, then they can cover diverse content, but they cannot provide personalized recommendations for specific scenes
Solution Approach 1:
The patent segments both video content and user feedback data into corresponding segments. This segmentation preserves detailed user preference information by maintaining the association between specific video segments and user responses, preventing information loss while enabling personalized scene recommendations through structured data organization.
Solution Approach 2:
The system applies local quality analysis by evaluating user preferences for each video segment individually rather than applying uniform analysis to entire videos. This local approach preserves detailed preference information for specific scenes while maintaining versatility across diverse video content, as each segment is analyzed with appropriate granularity.
Data Source
AI summary
A video recommendation system is provided. The video recommendation system has: an interaction device configured to receive feedback information of a video from a user, wherein the videos are composed of a plurality of video segments; a feature calculating module configured to calculate object features of at least a first video segment corresponding to the feedback information in the plurality of video segments; and an analysis module configured to recommend at least one first recommended video segment to the user from the plurality of video segments according to the calculated object features and the feedback information.