Graduated Machine Learning Model Training Data Selection
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Solution Overview
Problem
Machine learning models in recommendation systems often fail to accurately recommend new content items due to insufficient training data, leading to inaccurate predictions and reduced effectiveness until sufficient historical data is collected, which can take weeks or months.
Innovation Solution
A graduated machine learning model is trained using training data sets with increasing levels of granularity, where simulation tests determine the threshold amount of target outputs needed to train the model, allowing for accurate predictions of new content items more quickly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a machine learning model is trained using sufficient historical training data to achieve accurate recommendations, then the prediction accuracy is improved, but the time required to collect and train the model increases significantly
Solution Approach 1:
The patent applies preliminary action by pre-collecting and organizing training data into multiple data sets with different granularity levels before they are needed for modeling. This allows the model to be quickly trained when new content items are introduced, eliminating the need to wait weeks or months for sufficient historical data to accumulate. The training data is prepared in advance in a structured format that enables rapid model adaptation.
Solution Approach 2:
The patent segments the training data into multiple data sets with different granularity levels (e.g., coarse-grained and fine-grained data sets). This segmentation allows the machine learning model to be trained incrementally, first on coarser data for quick initial training, then progressively on finer-grained data to improve accuracy. This resolves the contradiction by enabling accurate predictions without requiring the model to wait for all detailed data to accumulate.
2Reliability
If a machine learning model is trained gradually with increasing data granularity, then the recommendation effectiveness is improved, but the system complexity increases
Solution Approach 1:
The system segments the training process into multiple stages, each using data sets of different granularity levels. This structured segmentation provides a clear framework for managing complexity, where each stage has defined inputs and outputs. The multi-stage approach makes the complex gradual training process more manageable and systematic, improving recommendation effectiveness without overwhelming system complexity.
Solution Approach 2:
The patent implements dynamics by allowing the machine learning model to adapt its training process based on the availability and granularity of data. The system dynamically transitions between different training stages as data becomes available, enabling flexible adjustment of training intensity and data granularity. This dynamic approach improves recommendation effectiveness while keeping system complexity manageable through adaptive rather than rigid processes.
Data Source
AI summary
Simulation data associated with a simulation test performed with respect to a first set of training data is obtained. Responsive to a determination that the obtained simulation data satisfies one or more criteria, a second set of training data is obtained, where a size of the second set of training data meets or exceeds a size of the first set of training data. A machine learning model is trained using the second set of training data.