Personalized Video Recommendation via Topic Modeling
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Solution Overview
Problem
Current video recommendation systems face challenges in accurately recommending videos to users due to reliance on metadata, which may be incomplete or incorrect, and fail to fully utilize visual content of varying granularity, leading to poor recommendation results.
Innovation Solution
A method and system for personalized video recommendation that detects user viewing activities, represents user interests using a topic model, and generates a personalized video list based on user viewing histories and behaviors, incorporating visual and textual analysis to recommend videos effectively.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of manufacture
If metadata-based recommendation is used, then implementation is simple, but recommendation accuracy deteriorates due to incomplete or incorrect metadata
Solution Approach 1:
The patent introduces visual features and topic models as intermediary representations between raw video content and recommendation decisions. Instead of directly using unreliable metadata, the system extracts visual features from video frames and uses topic models to derive semantic meanings, creating a more reliable bridge between content and user preferences.
Solution Approach 2:
The patent replaces the mechanical metadata annotation process with automated visual analysis. Instead of manually creating or relying on pre-existing metadata, the system uses computer vision algorithms to automatically extract visual features and infer semantic information from video content itself.
2Measurement precision
If manual annotation is applied to videos without metadata, then recommendation accuracy improves, but time consumption and cost increase significantly
Solution Approach 1:
The patent enables videos to self-annotate through automated visual analysis. The system extracts visual features directly from video content and uses topic models to generate semantic descriptions automatically, eliminating the need for human annotators to manually tag each video while maintaining high recommendation accuracy.
Solution Approach 2:
The patent changes the parameters used for video representation from static metadata fields to dynamic visual features extracted from video frames. By analyzing visual parameters such as color histograms, texture features, and object detection results, the system automatically generates meaningful representations without manual intervention.
3Measurement precision
If visual content analysis is fully explored at multiple granularities, then recommendation accuracy improves, but system complexity increases
Solution Approach 1:
The patent segments visual analysis into multiple granularity levels: frame-level analysis for basic visual features, shot-level analysis for scene understanding, and video-level analysis for overall content characterization. This hierarchical segmentation allows the system to process visual information at different levels of detail without overwhelming complexity.
Solution Approach 2:
The patent adds the dimension of temporal analysis to visual content processing. By analyzing not only spatial features within frames but also temporal relationships between frames and shots, the system captures dynamic visual information that enhances recommendation accuracy without proportionally increasing complexity.
Data Source
AI summary
A method is provided for personalized video recommendation based on user interests modeling. The method includes detecting a viewing activity of at least one user of a content-presentation device capable of presenting multiple programs in one or more channels, and representing user interests of the at least one user by using a topic model. The method also includes discovering the user interests from user viewing histories, and generating a personalized video list of personalized video contents. Further, the method includes recommending the personalized video contents to the at least one user; and delivering the recommended personalized video to the at least one user such that the personalized video contents are presented on the content-presentation device.