Distributed Matrix Segmentation for Job Candidate Ranking
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Solution Overview
Problem
Existing systems face inefficiencies in predicting numerical outcomes in matrix-defined problem spaces, particularly in correlating values and ranking job candidates, due to the complexity of processing large matrices and the scarcity of negative examples.
Innovation Solution
The implementation of field-aware factorization machines (FFMs) with distributed training algorithms and system optimizations, allowing for efficient computation across multiple machines and the generation of negative samples, enables faster and more accurate predictions and rankings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If distributed training algorithms are implemented across multiple machines, then processing speed and scalability improve, but system complexity increases
Solution Approach 1:
The patent divides the large matrix into multiple submatrices and distributes them across multiple computation servers. Each server independently processes its assigned submatrix, enabling parallel computation that improves processing speed and scalability while managing system complexity through modular decomposition.
2Measurement precision
If negative samples are generated to improve prediction accuracy, then model performance improves, but data processing complexity increases
Solution Approach 1:
The system pre-generates negative samples by randomly sampling from the matrix before the main prediction process. This preliminary action ensures that negative examples are readily available when training the factorization machine, improving prediction accuracy without adding complexity during the actual prediction phase.
3Measurement precision
If field-aware factorization machines are used to handle matrix complexity, then prediction accuracy improves, but computational requirements increase
Solution Approach 1:
The patent implements field-aware factorization machines that treat different fields (columns) of the matrix differently by assigning specific factorization parameters to each field. This local differentiation improves prediction accuracy for specific feature types while optimizing computational resources by not applying uniform complex processing to all data.
Solution Approach 2:
The matrix is segmented into multiple fields, and the factorization machine processes each field with appropriate parameters. This segmentation allows the system to focus computational effort where needed while reducing overall computational requirements compared to processing the entire matrix uniformly.
4Stability of the object's composition
If large matrices are processed centrally, then data consistency is maintained, but processing time increases
Solution Approach 1:
The patent segments the large matrix into submatrices distributed across multiple servers, enabling parallel processing that reduces processing time. Data consistency is maintained through coordinated training algorithms that ensure all servers work toward a unified model, combining the benefits of distributed processing with data integrity.
Data Source
AI summary
Systems and methods for predicting feature values in a matrix are disclosed. In example embodiments, a server accesses a matrix, the matrix having multiple dimensions, one dimension of the matrix representing features, and one dimension of the matrix representing entities. The server separates the matrix into multiple submatrices along a first dimension, each submatrix including all cells in the matrix for a set of values in the first dimension. The server provides the multiple submatrices to multiple machines. The server computes, using each machine, a correlation between values in at least one second dimension of the matrix and a value for a preselected feature in the matrix, the correlation being used to predict the value for the preselected feature based on other values along the at least one second dimension. The server provides an output representing the computed correlation.