Task-Specific Entity Embeddings for Sparse Feature Enrichment
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Solution Overview
Problem
Traditional machine learning models for predictive tasks suffer from poor performance due to the use of sparse and uninformative predictive features, which rely on high-level descriptions that lack contextual information, leading to inefficient memory usage and reduced accuracy.
Innovation Solution
A feature engineering framework generates contextual embeddings tailored to specific predictive tasks by filtering and augmenting sparse data with contextual and informative textual descriptions, leveraging natural language processing to enhance the quality of predictive features.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional machine learning models use sparse predictive features with high-level descriptions, then memory storage requirements are reduced, but predictive accuracy deteriorates
Solution Approach 1:
The patent extracts only the most relevant contextual information from vast amounts of raw data by filtering predictive features based on task relevance. This selective extraction maintains memory efficiency while improving accuracy by removing irrelevant information and retaining only the most predictive contextual features.
Solution Approach 2:
The system performs preliminary feature engineering and contextual enrichment before the actual predictive modeling. By pre-processing and selecting relevant features in advance, the system reduces memory requirements during model training while ensuring high predictive accuracy through careful feature preparation.
2Productivity
If traditional machine learning models use sparse predictive features, then processing requirements are reduced, but feature interpretability deteriorates
Solution Approach 1:
The patent introduces contextual features as intermediary elements that bridge the gap between raw sparse identifiers and meaningful interpretations. These contextual features serve as mediators that enrich the original features with interpretable information while maintaining processing efficiency through selective feature engineering.
Solution Approach 2:
The system transforms sparse categorical features into enriched contextual representations by changing the parameter space of the features. This transformation adds interpretability dimensions while controlling processing requirements through targeted feature engineering rather than comprehensive data expansion.
3Device complexity
If traditional machine learning models use high-level descriptions of predictive identifiers, then data storage is simplified, but information quality deteriorates
Solution Approach 1:
The patent segments the information representation into distinct components: original sparse identifiers and derived contextual features. This segmentation allows the system to store simplified identifiers while maintaining access to rich contextual information through computed features, thereby reducing storage complexity without losing information quality.
Solution Approach 2:
The system performs preliminary contextual enrichment during the feature engineering phase, creating interpretable representations before storage. This pre-processing ensures that the stored data maintains high information quality while keeping the storage structure simple and organized.
4Device complexity
If traditional machine learning models rely on raw features without contextual augmentation, then feature engineering complexity is reduced, but predictive performance deteriorates
Solution Approach 1:
The patent implements preliminary contextual augmentation during the feature engineering phase, where raw features are enriched with contextual information before being used for predictive modeling. This pre-processing step improves predictive performance while the systematic approach to feature engineering manages the complexity through structured workflows.
Solution Approach 2:
The system transforms raw features into contextualized representations by changing the feature parameters and adding contextual dimensions. This transformation enhances predictive performance while the parameter transformation approach provides a manageable framework for feature engineering complexity.
Data Source
AI summary
Various embodiments of the present disclosure provide a feature engineering framework for generating task-specific contextual embeddings to improve machine learning model performance, while reducing memory requirements for the performance of a predictive task. The techniques may include identifying a plurality of entity predictive identifiers from historical data objects for an entity. The entity predictive identifiers are used with a task-specific data store to generate a textual entity document for the entity by concatenating a set of contextual text descriptions from the task-specific data store that correspond to a set of task relevant identifiers from the plurality of entity predictive identifiers. The techniques include generating, using a machine learning encoder model, a contextual entity embedding for the entity based on the textual entity document and then training, using the contextual entity embedding, a machine learning classification model for the performance of the predictive task.