Personalized Action Suggestions Using ML Pre-Ranking
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Solution Overview
Problem
Existing personalization technologies focus on delivering relevant content to users but fail to suggest personalized actions based on their unique routines and behaviors, lacking dynamic adaptation to individual user needs.
Innovation Solution
A method and system that utilize machine learning algorithms, such as DensiCube, K-means, and Random Forest, to analyze user behavior data and suggest actions by filtering available actions, executing machine learning models, and sorting them based on scores, offering users relevant options tailored to their habits and permissions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning models are executed on each possible action to generate personalized suggestions, then the relevance and personalization of user interface suggestions is improved, but the computational complexity and processing time increases
Solution Approach 1:
The system pre-processes user behavior data and pre-ranks possible actions using machine learning models before the user needs suggestions. By performing computations in advance and caching results, the system reduces real-time computational burden while maintaining personalized recommendation quality.
Solution Approach 2:
The system divides the large set of possible actions into smaller subsets based on user context, behavior patterns, and relevance thresholds. Machine learning models are executed on these segmented subsets rather than the entire action space, reducing computational complexity while preserving personalization for the most relevant actions.
2Measurement precision
If the system analyzes extensive user behavior data to build accurate machine learning models, then the accuracy of action suggestions is improved, but the data processing time and computational resources increase
Solution Approach 1:
The system processes only the most relevant portions of user behavior data that have the highest impact on suggestion accuracy. By identifying and focusing on key behavioral patterns and features rather than processing all available data, the system achieves high accuracy with reduced processing time.
Solution Approach 2:
The system dynamically adjusts data processing parameters such as sampling rates, feature selection criteria, and model complexity based on user context and available resources. This allows the system to maintain high suggestion accuracy while adapting computational effort to match time constraints and resource availability.
3Measurement precision
If the system filters and sorts all possible actions through machine learning models, then the relevance of suggested actions is improved, but the processing speed decreases
Solution Approach 1:
The system extracts and prioritizes only the most promising actions from the full set of possible actions based on initial filtering criteria and user context. Machine learning models are then applied only to these extracted candidates, maintaining high relevance while significantly improving processing speed by avoiding exhaustive evaluation of all actions.
Data Source
AI summary
A unique implementation of a machine learning application for suggesting actions for a user to undertake is described herein. The application transforms a history of user behavior into a set of models that represent user actions given a set of parameters. These models are then used to suggest that users in a payments or banking environment take certain actions based on their history. The models are created using the DensiCube, random forest or k-means algorithms.