Dynamic Latent Vector Allocation for Adaptive Feature Mapping
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Solution Overview
Problem
Existing data processing methods fail to effectively differentiate between user features of varying informative value when constructing target vectors, leading to inefficient representation and adaptability in dynamic data environments.
Innovation Solution
A performance-based dynamic vector construction method that uses machine learning to identify and replace original attributes with alternative attributes based on assessment criteria, dynamically adjusting the size and importance of feature vectors during training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If fixed size user feature vectors are mapped to target vectors with static function mapping, then the system structure is simple and stable, but the system cannot adapt to different user features of varying informative value
Solution Approach 1:
The patent implements dynamic vector construction by allowing the target vector size and feature mappings to change based on user characteristics and performance metrics. The system dynamically determines which user features to include and their corresponding target vector entries, replacing static function mapping with adaptive, performance-based mapping that adjusts to different user profiles and contexts.
Solution Approach 2:
The system changes the parameters of the target vector construction process by adjusting the number of vector entries, the mapping relationships between user features and target entries, and the selection criteria based on performance assessments. This allows the system to optimize vector representation for different scenarios while maintaining a manageable construction process through structured parameter adjustment.
2Productivity
If all user feature vectors are considered equally important, then the mapping process is simple and fast, but the representation efficiency is reduced
Solution Approach 1:
The patent applies local quality by treating different user features differently based on their informative value. Instead of uniform processing, the system identifies and prioritizes specific user features that contribute more to prediction accuracy, allocating more resources and attention to these high-value features while reducing processing for less important ones, thereby improving overall efficiency.
Solution Approach 2:
The system changes the processing parameters dynamically by adjusting the importance weights, selection criteria, and resource allocation for different user features based on their assessed informative value. This parameter adjustment allows the system to focus computational resources on the most impactful features, reducing training time while improving representation efficiency.
3Reliability
If the target vector construction is static, then the system is easier to implement and maintain, but it lacks performance optimization capability
Solution Approach 1:
The patent implements feedback mechanisms by continuously assessing the performance of the target vector construction and using this information to refine and optimize the mapping relationships. The system monitors prediction accuracy and other performance metrics, then adjusts the vector construction strategy accordingly, creating a closed-loop system that improves representation accuracy while managing complexity through structured feedback-driven optimization.
Data Source
AI summary
The present teaching relates to method, system, and computer programming product for dynamic vector allocation. Machine learning is conducted using training data constructed based on a target vector having a plurality of feature entries, wherein each of the plurality of feature entries is mapped from at least one original attribute from one or more original source vectors. A feature entry in the target vector is identified based on a first criterion associated with an assessment of the machine learning, for replacing the corresponding at least one original attribute from the one or more original source vectors. At least one alternative attribute from alternative source vectors based on a second criterion is determined, wherein the at least one alternative attribute is to be mapped to the feature entry of the target vector. The feature entry of the target vector is populated based on the at least one alternative attribute.