Proximal Factorization Machine Interface Engine for Sparse Data Prediction
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Solution Overview
Problem
Existing predictive models, such as linear regression, are ineffective in capturing higher-order dependencies and are inefficient for binary classification problems, leading to inaccurate predictions and high memory or slow evaluation times.
Innovation Solution
A combined interface engine that integrates linear and nonlinear prediction models, utilizing factorization machines to capture higher-order interactions and sparse data, with parameter estimation through FTRL-Proximal regularization for efficient updates and low storage footprint.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If linear regression models are used for prediction, then the model is simple and computationally efficient, but the model fails to capture higher-order dependencies between explanatory variables leading to inaccurate predictions
Solution Approach 1:
The patent combines linear regression models with factorization machine models into a hybrid prediction system. The linear component captures first-order effects while the factorization machine component captures higher-order interactions between features, merging the simplicity of linear models with the expressive power of factorization machines to achieve both computational efficiency and high prediction accuracy
Solution Approach 2:
The prediction model uses a composite structure combining two different modeling approaches: linear regression for capturing additive effects and factorization machines for capturing interaction effects. This composite modeling strategy allows the system to leverage the strengths of both approaches while mitigating their individual weaknesses
2Measurement precision
If specialized predictive models are used for binary classification, then prediction accuracy improves, but memory overhead increases and evaluation time slows down
Solution Approach 1:
The patent optimizes the factorization machine parameters and uses regularization techniques to control model complexity. By carefully tuning parameters such as the regularization strength and factorization dimension, the system achieves high binary classification accuracy while maintaining efficient evaluation speed and reasonable memory usage
3Measurement precision
If more accurate prediction models are implemented, then prediction quality improves, but system resource consumption and runtime response time worsen
Solution Approach 1:
The prediction model is segmented into distinct components: a linear regression component for capturing main effects and a factorization machine component for capturing interactions. This segmentation allows the system to process features efficiently through specialized computation paths, reducing overall runtime while maintaining high prediction quality
Data Source
AI summary
Techniques are described for training and evaluating a proximal factorization machine engine. In one or more embodiments, the engine receives a set of training data that identifies a set of actions taken by a plurality of users with respect to a plurality of items. The engine generates, for a prediction model, (a) a first set of model parameters representing relationships between features of the plurality of users and the set of actions, and (b) a second set of model parameters representing interactions between different features of the plurality of users and the plurality of items. For each respective item in a plurality of items, the engine computes a probabilistic score based on the model parameters. The engine selects and presents a subset of items based on the probabilistic scores.