Personalized Action Recommendation Models Using User Segmentation
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Solution Overview
Problem
Current predictive models face challenges in efficiently generating personalized action recommendations across diverse domains due to the vast amount of available data, requiring significant resources and time, and struggle to balance model performance and generalizability while identifying relevant features for accurate predictions.
Innovation Solution
A system and method that utilizes machine learning models, such as cohort matching and regression models, to identify user features, assign users to segments, and rank recommended actions based on expected value, probability, and cost, enabling efficient generation of personalized action recommendations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional predictive modeling methods are used to analyze large data sets, then model accuracy can be improved, but resource consumption and time requirements increase significantly
Solution Approach 1:
The patent segments the large data set into multiple smaller partitions and processes them in parallel using distributed computing frameworks. This allows the system to maintain high prediction accuracy while significantly reducing the time and computational resources required by dividing the overall modeling task into manageable chunks that can be executed concurrently across multiple processors or machines.
Solution Approach 2:
The system performs preliminary data processing, feature selection, and model preprocessing steps before the main predictive modeling execution. By pre-processing data, selecting relevant features in advance, and preparing model templates beforehand, the system reduces the computational burden during actual model generation, thereby improving efficiency without compromising accuracy.
2Adaptability or versatility
If generic predictive models are used to apply to wide variety of data sets, then model generalizability is improved, but model performance on specific domains decreases
Solution Approach 1:
The patent implements dynamic model adaptation where the predictive model automatically adjusts its parameters, features, and structure based on the characteristics of the input data set. This allows the same base model to generalize across different domains while maintaining high accuracy within each specific domain by dynamically tailoring to the data at hand rather than being fixed in a single configuration.
Solution Approach 2:
The system changes model parameters, feature sets, and hyperparameters based on the specific domain and data characteristics being analyzed. By automatically adjusting these parameters according to the input data, the model achieves both broad generalizability across different domains and specialized accuracy within each domain, resolving the trade-off between being too generic and too specific.
3Measurement precision
If manual feature selection and model building processes are used, then model quality can be improved, but time and resource requirements increase
Solution Approach 1:
The patent implements automated feature selection and model building processes that perform tasks traditionally requiring manual expert intervention. The system automatically selects relevant features, determines optimal model types, tunes hyperparameters, and validates results without human input, thereby maintaining high model quality while dramatically reducing the time and expertise required for model development.
Solution Approach 2:
The system replaces manual mechanical processes of feature selection and model building with automated computational algorithms and machine learning techniques. By substituting human expert analysis with automated systems that can process data faster and scale more efficiently, the patent maintains model quality while reducing development time and resource requirements.
Data Source
AI summary
Methods, systems, and computer-readable media for generating a personalized action recommendation are provided. The method acquires a request for a service that is associated with a user and the user's condition. The method then identifies one or more features of the user based on stored user information. The method next assigns the user to a segment based on the identified one or more features, generates a set of one or more recommended actions for the user based on the segment, and determines an expected value of each of the one or more recommended actions. The method determines a rank of the one or more recommended actions based on the expected value of each of the one or more recommended actions, and outputs a recommended action with a highest expected value for the user in response to the request for the service.