Personalized Recommendation System for User Behavior Inefficiencies
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Solution Overview
Problem
Conventional recommendation systems struggle to identify inefficiencies in user behavior and provide personalized recommendations that effectively remediate these inefficiencies, leading to generic and ineffective suggestions for users.
Innovation Solution
A computer-implemented method and system that uses machine learning models to analyze user behavior patterns, classify them based on personas, identify inefficiencies impacting user goals, and generate personalized recommendations to remediate these inefficiencies.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If conventional recommendation systems are used, then the system structure is simple, but the recommendation effectiveness is poor and cannot identify inefficiencies in user behavior
Solution Approach 1:
The system segments the recommendation process into distinct functional modules: data collection module, behavior pattern generation module (using first ML model), classification module (using personas), inefficiency identification module, and recommendation generation module (using second ML model). This segmentation allows each module to specialize in specific tasks, improving overall reliability while managing complexity through modular architecture.
Solution Approach 2:
The patent introduces intermediaries such as behavior patterns and personas as mediating structures between raw user data and final recommendations. These intermediaries transform complex raw data into structured representations that can be systematically analyzed and matched against inefficiency criteria, enabling reliable recommendations without direct complex analysis of all raw data.
2Measurement precision
If personalized recommendations are generated based on detailed behavior analysis, then recommendation precision is improved, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary actions by pre-defining multiple personas and inefficiency criteria before actual recommendation generation. Behavior patterns are generated and classified in advance using trained ML models, so that when real-time recommendations are needed, the system only needs to match current behavior against pre-established patterns and criteria rather than performing complete analysis from scratch.
Solution Approach 2:
The system applies partial action by focusing analysis only on the most relevant behavior aspects for each specific recommendation context. Instead of analyzing all possible user behaviors comprehensively, the system identifies and analyzes only the specific behavior patterns relevant to the current recommendation task, reducing processing time while maintaining sufficient precision.
3Measurement precision
If multiple data sources and complex ML models are used, then recommendation accuracy is improved, but system complexity and data processing requirements increase
Solution Approach 1:
The system employs universal data structures and standardized processing pipelines that can handle multiple data sources through a common framework. The behavior pattern generation and classification modules are designed to work with diverse data types (transactional, demographic, behavioral) using unified processing logic, reducing the complexity increase that would result from treating each data source separately.
Data Source
AI summary
A method for generating predictive and event-based action recommendations includes receiving, from at least one data source, input data representative of a user; generating, using a first machine learning model, a user behavior pattern for the user based on the input data representative of the user; classifying the user behavior pattern based on one or more personas representative of user characteristics; identifying, based on at least the input data and the one or more personas, an inefficiency in the user behavior pattern impacting a goal of the user; and generating, using a second machine learning model, a personalized recommendation for the user to remediate the inefficiency.