Closed-Form Preference Model for Recommendation Accuracy
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Solution Overview
Problem
Recommender deep neural networks for recommendation systems require significant time and processing resources to train and can be difficult to understand, and they degrade in accuracy when new user preferences are established, making them inefficient and opaque.
Innovation Solution
A preference prediction model is trained using a closed-form solution based on a Gram matrix, which allows for non-iterative training and provides a linear regression model that is easier to understand, reducing training time and processing resources while maintaining accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a recommender deep neural network is used to predict user preferences, then the accuracy of recommendations is improved, but the training time and processing resources required increase significantly
Solution Approach 1:
The patent replaces the iterative training mechanism of deep neural networks with a closed-form solution approach. Instead of using gradient descent and multiple training iterations to optimize neural network parameters, the invention directly computes the optimal parameters using matrix operations and Lagrangian minimization, eliminating the need for iterative processing and significantly reducing training time.
Solution Approach 2:
The invention changes the mathematical approach from iterative optimization to closed-form parameter estimation. By formulating the problem as a constrained optimization problem and solving it analytically through Lagrangian minimization, the system obtains direct parameter values without iteration, transforming the training process from a time-consuming computational search to a direct calculation.
2Measurement precision
If a recommender deep neural network is used to predict user preferences, then the accuracy of recommendations is improved, but the processing resources required increase significantly
Solution Approach 1:
The patent replaces the iterative training mechanism of deep neural networks with a closed-form solution approach. Instead of using gradient descent and multiple training iterations to optimize neural network parameters, the invention directly computes the optimal parameters using matrix operations and Lagrangian minimization, eliminating the need for iterative processing and significantly reducing training time.
Solution Approach 2:
The invention uses a simplified linear model that copies the functional capability of deep neural networks for preference prediction but with much fewer computational requirements. The linear model with closed-form solution achieves the same predictive accuracy without the computational complexity of training millions of neural network parameters.
3Measurement precision
If a recommender deep neural network is used, then recommendation accuracy can be maintained, but the model becomes difficult to understand and interpret
Solution Approach 1:
The patent uses a simple linear model that is computationally inexpensive and easily interpretable, replacing the complex deep neural network. The linear model with its closed-form solution provides transparent parameter estimates that can be directly analyzed, while maintaining sufficient accuracy for recommendation purposes without the interpretability black box of neural networks.
Data Source
AI summary
In various embodiments, a training application generates a preference prediction model based on an interaction matrix and a closed-form solution for minimizing a Lagrangian. The interaction matrix reflects interactions between users and items, and the Lagrangian is formed based on a constrained optimization problem associated with the interaction matrix. A service application generates a first application interface that is to be presented to the user. The service application computes predicted score(s) using the preference prediction model, where each predicted score predicts a preference of the user for a different item. The service application then determines a first item from the items to present to the user via an interface element included in the application interface. Subsequently, the service application causes a representation of the first item to be displayed via the interface element included in the application interface.