Hybrid Optimization Training of Multinomial Logit Models
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Solution Overview
Problem
Current machine learning models, particularly multinomial logit models, require significant time and computing power for training, making them inflexible to real-time changes in trends and preferences, leading to inconsistent predictions over time.
Innovation Solution
A hybrid optimization training process using a first-order Barzilai-Borwein (BB) method with an adaptive step size is employed to iteratively train machine learning models, allowing for faster convergence and more frequent updates, incorporating contextual features for improved item recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional training methods are used for machine learning models, then model accuracy and consistency are improved, but training time and computing power requirements increase significantly
Solution Approach 1:
The patent applies parameter changes by transitioning from traditional gradient descent optimization to the Barzilai-Borwein (BB) optimization method. This changes the mathematical parameters of the training process, specifically the step size calculation and convergence criteria, enabling faster training times while maintaining model accuracy and prediction consistency.
Solution Approach 2:
The patent implements dynamics by using an adaptive step size in the Barzilai-Borwein optimization process. The step size dynamically adjusts during training based on the curvature of the loss function, allowing the model to converge faster and adapt to changing data distributions without sacrificing prediction reliability.
2Reliability
If models are trained extensively to ensure accuracy, then prediction reliability is improved, but the ability to react to real-time changes is reduced
Solution Approach 1:
The patent enables real-time adaptability through the dynamic nature of the Barzilai-Borwein optimization method. The adaptive step size allows the model to quickly adjust to new data patterns and changing user preferences without requiring complete retraining, thus maintaining both accuracy and real-time responsiveness.
Solution Approach 2:
The patent applies preliminary action by pre-training the model with extensive data to establish accurate baseline predictions. The optimized training method then enables rapid fine-tuning and adaptation to new patterns, combining the benefits of extensive pre-training with quick real-time adjustments.
3Stability of the object's composition
If traditional optimization methods are used for model training, then convergence stability is maintained, but training speed and computational efficiency are reduced
Solution Approach 1:
The patent changes the optimization parameters by implementing the Barzilai-Borwein method, which dynamically adjusts the step size based on the Hessian matrix approximation. This parameter change enables faster convergence (higher productivity) while maintaining stability through the adaptive nature of the BB algorithm that prevents oscillations and divergence.
Data Source
AI summary
Systems and methods for hybrid optimization of training ranking models is disclosed. A training dataset including a plurality of anchor items, a plurality of recommended item sets, and ground truth data is obtained from a database. A base machine learning model including a step function configured to determine a relevance score is iteratively trained to generate a trained ranking model. The plurality of anchor items and the plurality of recommended item sets are provided as an input to the base machine learning model and the ground truth is provided as a target output. The step function is trained using an adaptive step size according to a first order Barzilai-Borwein (BB) method and a line search method. The trained ranking model is stored in non-transitory memory.