Address Recommendation System Using Context Vectors and Reinforcement Learning

Solution Overview

Problem

Inefficient and error-prone manual processing of purchase orders due to the need for users to sift through large numbers of addresses to match extracted addresses with existing customer addresses, leading to significant time consumption and potential errors.

Innovation Solution

A recommendation system that generates address recommendations using a context vector and recommendation model, incorporating user feedback to iteratively improve address matching, employing a multi-arm bandit upper confidence interval reinforcement learning algorithm to minimize manual intervention and enhance accuracy.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Measurement precision

If manual address matching is performed by users, then flexibility and accuracy can be maintained, but time consumption increases significantly and error rates rise

Engineering Contradiction:

Improveaddress matching accuracyVSAvoidpurchase order processing time

Core Design Contradiction:

Measurement precisionVSLoss of time

Solution Approach 1:

The system performs self-service by automatically generating address recommendations using machine learning models without requiring manual intervention for each matching decision. The recommendation system autonomously processes addresses, generates suggestions, and learns from user feedback to improve accuracy over time, eliminating the need for users to manually sift through large numbers of addresses.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system incorporates feedback mechanisms where users can confirm or correct address recommendations. This feedback is used to retrain and refine the machine learning models, continuously improving the accuracy of address matching. The feedback loop allows the system to learn from real-world usage patterns and adapt to evolving address formats and customer preferences.

Inventive Principle:

Principle #23Feedback

2Productivity

If automated address matching is implemented, then processing speed increases, but reliability may decrease due to potential errors in automated systems

Engineering Contradiction:

Improvepurchase order processing efficiencyVSAvoidaddress matching reliability

Core Design Contradiction:

ProductivityVSReliability

Solution Approach 1:

The system performs self-service by automatically generating address recommendations using machine learning models without requiring manual intervention for each matching decision. The recommendation system autonomously processes addresses, generates suggestions, and learns from user feedback to improve accuracy over time, eliminating the need for users to manually sift through large numbers of addresses.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system incorporates feedback mechanisms where users can confirm or correct address recommendations. This feedback is used to retrain and refine the machine learning models, continuously improving the accuracy of address matching. The feedback loop allows the system to learn from real-world usage patterns and adapt to evolving address formats and customer preferences.

Inventive Principle:

Principle #23Feedback

3Measurement precision

If users manually review addresses, then accuracy can be maintained, but the complexity of the processing workflow increases

Engineering Contradiction:

Improveaddress verification accuracyVSAvoidprocessing workflow complexity

Core Design Contradiction:

Measurement precisionVSDevice complexity

Solution Approach 1:

The system performs self-service by automatically generating address recommendations using machine learning models without requiring manual intervention for each matching decision. The recommendation system autonomously processes addresses, generates suggestions, and learns from user feedback to improve accuracy over time, eliminating the need for users to manually sift through large numbers of addresses.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system performs preliminary action by pre-processing and validating addresses before they are presented to users. The machine learning model generates pre-ranked address recommendations based on multiple factors such as address format, historical data, and geographic information, so that users only need to review the top suggestions rather than manually processing the entire list.

Inventive Principle:

Principle #10Preliminary action

Data Source

PatentUS20240232990A9Method and system for recommending addresses during purchase order processing

Publication Date: 2024.07.11 DELL PROD LP

AI summary

Techniques described herein relate to a method for generating address recommendations. The method includes obtaining, by a recommendation system, an address recommendation request associated with an address, wherein the address is associated with a user; in response to obtaining the address recommendation request: generating a context vector associated with the address; generating an address recommendation based on the context vector using a recommendation model; obtaining user feedback associated with the address recommendation; generating a reward based on the user feedback; and updating the recommendation model based on the context vector, the address recommendation and the reward.