Content Recommendation Models with Normalized Logit Calibration
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Neural networks in recommendation systems often produce overconfident predictions, leading to challenges in accurately suggesting targeted contents relevant to users.
Innovation Solution
A prediction tool implements a unique training technique that normalizes and calibrates neural network models during the learning process using a regularized loss algorithm with a regularization term to suppress overconfidence.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If neural networks are used for prediction in recommendation systems, then high-ranking performance is achieved, but overconfident predictions occur leading to reduced reliability
Solution Approach 1:
The patent modifies the loss function parameters by incorporating a temperature parameter (T) and a regularization term with weight (lambda) to control the confidence calibration. The temperature scaling transforms the logits to adjust prediction confidence, while the regularization term penalizes overconfident predictions during training, thereby improving reliability without sacrificing accuracy
Solution Approach 2:
The patent implements a feedback mechanism where the model's prediction confidence is continuously monitored and adjusted through the calibrated loss function. The regularization term provides feedback by penalizing deviations from target confidence levels, enabling the model to self-correct overconfident predictions during training iterations
2Reliability
If post-learning calibration methods are applied to reduce overconfidence, then prediction reliability improves, but computational complexity increases
Solution Approach 1:
The patent performs calibration actions during the training phase rather than as a separate post-processing step. The temperature scaling parameters and regularization terms are optimized concurrently with model training, eliminating the need for separate calibration procedures and reducing overall computational complexity
Solution Approach 2:
The patent merges the calibration process with the training process by integrating the temperature scaling and regularization terms directly into the loss function. This unified approach combines model learning and confidence calibration into a single optimization process, reducing computational overhead
3Reliability
If regularization term is added to loss function during training, then overconfidence is suppressed and reliability improves, but training complexity increases
Solution Approach 1:
The patent introduces a regularization term with a controllable weight parameter (lambda) that can be adjusted to balance the trade-off between reducing overconfidence and maintaining training simplicity. By tuning this parameter, the system can control the impact of the regularization term on training complexity while achieving the desired reliability improvement
Data Source
AI summary
Systems and methods for training a predictive model for suggesting content for users. In particular, a computer device may receive a first set of data, the first set of data indicative of one or more user engagement metrics, generate a predictive model based on the first set of data using a regularized loss algorithm, the regularized loss algorithm including a loss function with a regularization term, the regularization term being a normalized logit loss to adjust the loss function, receive a second set of data, generate an output using the predictive model based on the second set of data, evaluate the output of the predictive model by determining evaluation metrics indicative of predicted performance and reliability of the predictive model, adjust strength of regularization of the regularized loss algorithm based on the evaluation metrics, and train the predictive model using the regularized loss algorithm.