ML-Based Item-Seller Recommendation for Event Campaigns

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Solution Overview

Problem

Existing sellers face challenges in manually selecting items for advertising campaigns, as they are overwhelmed by their vast assortments and limited by the number of submissions allowed per event.

Innovation Solution

A system utilizing a machine learning model to identify item-seller combinations with high sale probabilities, allocating these combinations to upcoming shopping events, and generating customized lists of items for sellers to create effective advertising campaigns.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of operation

If sellers manually select items for advertising campaigns, then they can control item selection, but they are overwhelmed by vast assortments and limited by submission limits

Engineering Contradiction:

Improveease of item selectionVSAvoidcomplexity of item selection process

Core Design Contradiction:

Ease of operationVSDevice complexity

Solution Approach 1:

The system enables automatic item selection through machine learning models that autonomously analyze seller assortments and generate optimized item recommendations, eliminating the need for manual selection processes while respecting submission limits

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The patent replaces the manual mechanical selection process with an automated machine learning-based system that uses algorithms to identify high-probability sale items, substituting human effort with computational intelligence

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

2Quantity of substance

If sellers submit more items for campaigns, then they increase coverage, but they exceed submission limits per event

Engineering Contradiction:

Improvenumber of item submissionsVSAvoidflexibility in item selection

Core Design Contradiction:

Quantity of substanceVSAdaptability or versatility

Solution Approach 1:

The system changes the parameter of item selection from manual criteria to machine learning predicted sale probability, enabling optimal selection within fixed submission limits by prioritizing items with highest predicted performance

Inventive Principle:

Principle #35Parameter changes

3Reliability

If sellers focus on high-sale-probability items, then they increase campaign success rate, but they may miss diverse product opportunities

Engineering Contradiction:

Improvecampaign success probabilityVSAvoidproduct portfolio diversity

Core Design Contradiction:

ReliabilityVSAdaptability or versatility

Solution Approach 1:

The system applies different selection criteria to different items within a seller's assortment, identifying specific high-probability items for each campaign while maintaining overall portfolio diversity through customized recommendations

Inventive Principle:

Principle #3Local quality

Data Source

PatentUS20250037183A1System and method for recommending items to create advertising campaigns for upcoming events

Publication Date: 2025.01.30 WALMART APOLLO LLC

AI summary

Systems and methods for recommending items to create advertising campaigns for upcoming events are disclosed. In some embodiments, a disclosed method comprises: identifying at least one upcoming shopping event; determining, based on a machine learning model generated irrespective of any shopping event, a plurality of item-seller combinations each formed by a respective item and a respective seller; performing an allocation of at least one of the item-seller combinations to the at least one upcoming shopping event; generating, for a seller, a customized list of items associated with the at least one upcoming shopping event based on the allocation; and transmitting the customized list of items for the seller to create an advertising campaign.