Multivariate Random Forest Regression for Assistance Recommendations

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Solution Overview

Problem

Traditional data management systems fail to provide real-time personalized assistance offerings to users due to technical difficulties in identifying relevant services without excessive resource consumption, leading to user frustration and underutilization of available products and services.

Innovation Solution

A system utilizing historical user data and supervised machine learning methods, specifically a multivariate random forest regression process, to train an assistance offering recommendation model, which replaces zero selection rates with negative values to enhance accuracy in recommending relevant assistance offerings to current users based on their preferences.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If traditional data management systems provide the same static assistance offerings to all users, then resource consumption is reduced, but user satisfaction and utilization of services deteriorate

Engineering Contradiction:

Improveuser satisfactionVSAvoidsystem complexity

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The system performs preliminary actions by pre-processing user interaction data and pre-computing recommendation models during off-peak times. Historical user data is collected and processed in advance to build user profiles and preference models, so that when users access the system, personalized assistance offerings can be generated quickly without real-time computational burden.

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system creates simplified copies of user profiles and preference data that can be quickly queried and matched with assistance offerings. Instead of analyzing complete user histories in real-time, the system uses pre-generated user profile copies that capture essential preferences and behaviors, enabling fast personalized recommendation generation.

Inventive Principle:

Principle #26Copying

2Ease of operation

If traditional data management systems provide personalized assistance offerings in real-time, then user satisfaction improves, but processing resources and memory consumption increase excessively

Engineering Contradiction:

Improveuser satisfactionVSAvoidprocessing resources

Core Design Contradiction:

Ease of operationVSUse of energy by moving object

Solution Approach 1:

Inventive Principle:

Principle #10Preliminary action

Solution Approach 2:

The system updates user profiles and preference models periodically rather than continuously in real-time. User interaction data is collected and processed in periodic batches, allowing the system to maintain personalized recommendations while avoiding continuous heavy computational loads. This periodic processing significantly reduces processing resource consumption compared to real-time personalized recommendation generation.

Inventive Principle:

Principle #19Periodic action

3Adaptability or versatility

If traditional data management systems cycle through or present random assistance offerings, then some variety is provided, but relevance to individual user needs deteriorates

Engineering Contradiction:

Improvevariety of offeringsVSAvoidrelevance accuracy

Core Design Contradiction:

Adaptability or versatilityVSMeasurement precision

Solution Approach 1:

The system applies local quality by tailoring assistance offerings to each user's specific preferences, behaviors, and needs. User profiles capture individual characteristics and preferences, allowing the system to select and personalize assistance offerings for each user rather than using uniform random selections. This ensures that the variety of offerings is relevant to each individual user's context and requirements.

Inventive Principle:

Principle #3Local quality

Solution Approach 2:

The system implements feedback mechanisms where user interactions with assistance offerings are tracked and used to refine future recommendations. When users interact with (or ignore) assistance offerings, this feedback is incorporated into their user profiles and preference models, improving the accuracy and relevance of subsequent personalized recommendations while maintaining variety.

Inventive Principle:

Principle #23Feedback

Data Source

PatentUS11574315B2Method and system for recommending assistance offerings

Publication Date: 2023.02.07 INTUIT INC

AI summary

A method and system identify assistance offerings that are likely to be relevant to users of a data management system. The method and system utilize a multivariate random forest regression machine learning process to train an assistance offerings recommendation model to recommend relevant assistance offerings to users of the data management system. The multivariate random forest regression machine learning process replaces zero values in the training set data with negative numbers to increase the accuracy of the machine learning process.