Video Tag Recommendation With Dynamic Tag Libraries
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing video classification methods rely heavily on supervised learning with labeled data and require retraining when tag systems are adjusted, lacking flexibility in adapting to real-time requirements.
Innovation Solution
A video tag recommendation model is trained using a video feature extraction network and a tag feature extraction network, with positive and negative samples to learn associations without relying on accurate labeling, allowing for dynamic tag libraries and improved flexibility.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If supervised learning with labeled data is used for video classification, then classification accuracy can be achieved, but the model lacks flexibility when tag systems need to be adjusted
Solution Approach 1:
The patent transitions from a static classification model to a dynamic recommendation model. The video tag recommendation model continuously learns from user interaction data and automatically adapts to changing tag systems without requiring manual retraining. The model dynamically adjusts its recommendations based on real-time user behavior patterns, enabling flexible adaptation to new tag categories and evolving content trends.
Solution Approach 2:
The system performs self-optimization by automatically learning from user interactions and adjusting its own parameters. The recommendation model continuously refines its understanding of video-tag relationships through user feedback, eliminating the need for external intervention to update the model when tag systems change. This self-service capability maintains high accuracy while providing adaptability.
2Adaptability or versatility
If a classification model is retrained when tag system is adjusted, then the model can adapt to new tags, but the process is time-consuming and reduces productivity
Solution Approach 1:
The system pre-processes and stores user interaction data in a database, preparing it for continuous learning. By maintaining a historical record of user behaviors and preferences, the model can immediately leverage this pre-collected data when tag system adjustments occur, eliminating the need for complete retraining and reducing update time.
Solution Approach 2:
The recommendation model operates continuously, constantly learning from user interactions and refining its parameters in real-time. This continuous learning process ensures that the model is always up-to-date with current user preferences and tag system changes, eliminating the discontinuous nature of periodic retraining and maintaining high productivity.
3Measurement precision
If multi-modality fusion features are extracted, then the model achieves better accuracy in rapidly changing scenarios, but the device complexity increases
Solution Approach 1:
The patent divides the complex multi-modality fusion task into separate processing stages. Different modalities (visual, auditory, textual) are extracted and processed independently through dedicated feature extraction networks, then merged in a subsequent fusion stage. This segmentation reduces the complexity of any single component while achieving the benefits of multi-modality fusion.
Solution Approach 2:
The patent introduces intermediate feature representation layers that serve as mediators between raw multi-modality data and final tag recommendations. These intermediate representations simplify the complex relationships between different modalities and tags, making the overall system more manageable and computationally efficient while maintaining high accuracy.
Data Source
AI summary
The present disclosure provides a method of training a video tag recommendation model, a method of determining a video tag, an electronic device, and a storage medium. The video tag recommendation model includes a video feature extraction network and a tag feature extraction network. The method of training the video tag recommendation model includes: obtaining a first video feature of a video sample by using the video feature extraction network; inputting a first tag as a positive sample to the tag feature extraction network, so as to obtain a first feature of the first tag; inputting a second tag as a negative sample to the tag feature extraction network, so as to obtain a second feature of the second tag; and training the video tag recommendation model according to the first video feature, the first feature, the second feature, and a predetermined loss function.