Local Low-Rank Matrix Imputation for Contextual AI Pipeline Recommendations
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Solution Overview
Problem
Existing computation pipeline recommendation systems are inefficient and inaccurate in modeling similarities and dissimilarities between computation pipelines and contexts, leading to high computational costs and loss of local low-rank properties in large-scale implementations, and are not scalable for evaluating numerous pipelines and contexts.
Innovation Solution
The local low-rank matrix imputation (Lori) framework segments recommendation matrices into local low-rank submatrices using robust principal Hessian directions, predicting missing performance data in high-dimensional matrices to provide accurate pipeline recommendations based on similarities and dissimilarities across pipelines and contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If existing computation pipeline recommendation systems are used to model similarities and dissimilarities between pipelines and contexts, then recommendation accuracy is improved, but computational costs increase and local low-rank properties are lost in large-scale implementations
Solution Approach 1:
The patent divides the large recommendation matrix into multiple local low-rank submatrices based on robust principal Hessian directions. This segmentation allows the system to process and model similarities in smaller, manageable blocks while preserving the overall low-rank structure, thereby reducing computational complexity and energy consumption while maintaining recommendation accuracy.
Solution Approach 2:
The patent applies local low-rank approximation to submatrices of the recommendation matrix, allowing different regions of the matrix to be modeled with appropriate complexity. This local quality approach enables the system to capture specific similarity patterns in different contexts without requiring the entire matrix to be processed at full complexity, thus reducing overall computational costs.
2Adaptability or versatility
If existing recommendation systems evaluate numerous pipelines and contexts, then recommendation coverage is improved, but scalability is reduced due to high computational costs
Solution Approach 1:
By segmenting the recommendation matrix into local low-rank submatrices, the system can efficiently handle large-scale data across numerous pipelines and contexts. Each submatrix can be processed independently, enabling parallel computation and improving scalability while maintaining comprehensive coverage of the evaluation space.
Solution Approach 2:
The patent transforms the recommendation matrix into a set of submatrices with reduced rank parameters, effectively changing the dimensionality and complexity parameters of the data structure. This parameter transformation enables the system to scale to numerous pipelines and contexts while reducing the computational burden through lower-rank approximations.
3Measurement precision
If manual configuration and evaluation of computation pipelines is performed, then recommendation accuracy is improved, but time consumption increases
Solution Approach 1:
The system performs automatic configuration and evaluation of computation pipelines by leveraging the local low-rank matrix imputation framework. The automated process uses the segmented submatrices to predict performance data and generate recommendations, eliminating the need for manual configuration while maintaining high accuracy through the mathematical models.
Solution Approach 2:
The patent pre-processes the recommendation matrix by segmenting it into local low-rank submatrices and pre-computes the necessary transformations. This preliminary action enables the system to quickly generate recommendations for new pipelines and contexts without requiring time-consuming manual evaluation, thus reducing overall time consumption while maintaining accuracy.
Data Source
AI summary
Contextual computation pipeline recommendation concepts are described. For example, a method can include obtaining an incomplete recommendation matrix that includes first performance data for different computation pipelines with respect to different contextual datasets. The incomplete recommendation matrix lacking second performance data for a defined computation pipeline with respect to a defined contextual dataset. The method can also include segmenting the incomplete recommendation matrix into local low-rank submatrices that lack the second performance data. The method can also include predicting the second performance data for at least one of the local low-rank submatrices to create a completed recommendation matrix that includes the first performance data and the second performance data. The method can also include ranking the defined computation pipeline and/or one or more of the different computation pipelines with respect to the defined contextual dataset and/or one or more of the different contextual datasets based on the completed recommendation matrix.