User Representation Model for Open Vocabulary Prediction
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Solution Overview
Problem
Machine learning models for predicting data labels face challenges with open-ended datasets where data labels and objects continuously evolve, requiring incorporation of user-specific bias and subjectivity, and existing models struggle to handle new vocabulary or users without retraining.
Innovation Solution
A system that trains machine learning models using user representation models to encode information about users and correlations between data labels and objects, incorporating techniques like convolutional neural networks, gated recurrent units, and Multimodal Tucker Fusion to generate accurate data label predictions, even for unseen data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If standard image classification models are used with user-assigned hashtags, then large scale multi-label training data can be obtained, but prediction accuracy deteriorates due to user-specific variation and subjectivity in hashtags
Solution Approach 1:
The model segments the prediction task by introducing user-specific parameters that separate user subjective preferences from general image-content relationships. This allows the system to handle user-specific variation while maintaining overall prediction accuracy through dedicated user adaptation components.
Solution Approach 2:
The patent applies local quality by making model parameters user-specific rather than uniform across all users. Each user receives customized parameters that account for their individual hashtagging patterns and preferences, improving prediction accuracy for each user while maintaining scalability through parameter sharing mechanisms.
2Measurement precision
If user-specific models with learned user embeddings are used to account for user subjectivity, then prediction accuracy improves, but the models cannot be used on new users without retraining
Solution Approach 1:
The patent achieves universality by designing a model architecture where user-specific parameters can be instantiated for any user without retraining the core model. The system maintains a universal framework that accommodates both existing and new users through parameter initialization and adaptation mechanisms, enabling the model to serve multiple users including unseen ones.
Solution Approach 2:
The model performs preliminary action by pre-learning general image-hashtag relationships and user preference patterns during training. This preliminary learning enables the system to quickly adapt to new users through parameter initialization and fine-tuning, rather than requiring complete retraining, thus improving both accuracy and adaptability.
3Ease of manufacture
If fixed hashtag vocabulary models are used, then model training is simplified, but the models cannot predict new hashtags without retraining
Solution Approach 1:
The patent applies dynamics by transitioning from a static fixed vocabulary approach to a dynamic system that can adapt to new hashtags. The model maintains a core trained vocabulary while incorporating mechanisms to handle unseen hashtags through user-specific adaptations and continuous learning, allowing the system to evolve with new vocabulary without complete retraining.
Solution Approach 2:
The system introduces an intermediary layer between the fixed vocabulary model and new hashtags. This intermediary consists of user-specific parameters and embedding spaces that can bridge known and unknown hashtags, allowing the model to predict new vocabulary elements while maintaining the simplicity of the original training process.
4Device complexity
If models learn single embeddings for each hashtag with fixed vocabulary, then training is more straightforward, but the models lack ability to correctly predict unseen hashtags
Solution Approach 1:
The patent implements nested doll by embedding user-specific parameters within the overall model structure. The user-specific embeddings are nested within the general image-hashtag prediction framework, allowing the system to maintain simple base model structure while adding layered complexity for handling unseen hashtags through user adaptation.
Solution Approach 2:
The system adds another dimension to the embedding space by introducing user-specific parameter spaces alongside the standard hashtag embeddings. This dimensional extension allows the model to capture user-specific patterns for unseen hashtags without fundamentally changing the core embedding structure, maintaining simplicity while improving prediction accuracy.
Data Source
AI summary
Systems and methods adapted for training a machine learning model to predict data labels are described. The approach includes receiving a first data set comprising first data objects and associated first data labels, and processing, with a user representation model, respective first data objects and associated data labels associated with a unique user representation by fusing the respective first data object and the associated first data labels. First data object representations of the respective first data objects are generated, and the first data object representations and the user representation model outputs are fused to create a user conditional object representation. The machine learning model updates corresponding parameters based on an error value based on a maximum similarity of the projections of the respective user conditional object representation and first data labels in a joint embedding space.