Predictive Model Recommendation Engine Using Gradient Ascent
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Existing predictive modeling solutions fail to effectively generate recommendations to change undesirable outcomes, as they are often complex and hindered by high dimensionality, making it challenging to overturn predicted results.
Innovation Solution
A computing device accesses historical inputs and results from a database, approximates a function, computes its gradient at a local maximum, and generates recommended inputs that produce a positive result, using methods like Multivariate Lagrange interpolation and gradient ascent to identify steepest ascent directions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If existing predictive modeling solutions are used, then prediction outcomes can be obtained, but they fail to effectively generate recommendations to change undesirable outcomes due to high complexity and dimensionality
Solution Approach 1:
The patent segments the complex predictive modeling problem into distinct functional components: an explanation module that analyzes model predictions and identifies influential features, and a recommendation module that generates actionable suggestions. This segmentation allows each module to specialize in specific tasks, reducing overall system complexity while improving operational effectiveness.
Solution Approach 2:
The patent introduces an intermediary explanation layer between the predictive model and the recommendation generation process. This explanation module acts as a mediator that translates complex model internals into interpretable feature importance scores, which then guide the recommendation engine. This intermediary structure simplifies the interface between components and enables effective recommendation generation without requiring direct access to complex model internals.
2Productivity
If existing predictive modeling solutions are used, then prediction outcomes can be obtained, but they hinder effective recommendation generation due to high dimensionality
Solution Approach 1:
The patent extracts only the essential information needed for recommendation generation from the high-dimensional predictive model outputs. The explanation module extracts feature importance scores and influential attributes, discarding redundant dimensional information. This extraction process maintains the critical signal while removing noise, enabling efficient recommendation generation without information loss.
Solution Approach 2:
The patent performs preliminary analysis of the predictive model outputs before generating recommendations. The explanation module pre-processes the high-dimensional data by identifying and ranking influential features in advance. This preliminary action reduces the dimensionality of the problem space before the recommendation engine operates, improving efficiency while preserving the essential information needed for effective recommendations.
3Measurement precision
If complex predictive models are used, then accurate predictions can be obtained, but it becomes challenging to understand and visualize the models for generating suggestions
Solution Approach 1:
The patent implements a feedback loop where the explanation module continuously analyzes model predictions and provides interpretable insights back to the recommendation system. This feedback mechanism translates complex model behaviors into understandable feature importance metrics, maintaining prediction accuracy while improving interpretability. The feedback enables the system to understand and visualize model decisions without sacrificing predictive performance.
Solution Approach 2:
The patent replaces direct inspection of complex model mechanics with an alternative explanation mechanism. Instead of attempting to visualize and understand the internal mechanics of complex predictive models, the system substitutes an explanation layer that derives interpretable insights from model outputs. This substitution maintains accuracy while making the system understandable and visualizable for recommendation generation.
Data Source
AI summary
Example embodiments relate to generating sets of recommended inputs for changing predicted results of a predictive model. The examples disclosed herein access, from a database, a historical set of inputs and results of a predictive model. A function is approximated based on the historical set of inputs and results, and a gradient of the function is computed using a result of the function with respect to a local maximum value of the function. A set of recommended inputs is generated based on the gradient of the function, where a recommended input produces a positive result of the function.