A Flat-Hierarchical Approach Based on Machine Learning Model for e-Commerce Product Classification

Harold Enrique Cotacallapa Mamani

doi:10.1109/ACCESS.2024.3400693

A Flat-Hierarchical Approach Based on Machine Learning Model for e-Commerce Product Classification

2024-01-01

1

OA

AI

H

Harold Enrique Cotacallapa Mamani

N

Nemías Saboya *

P

Paulo Canas Rodrigues

R

Rodrigo Salas

J

Javier Linkolk López‐Gonzales

DOI：10.1109/ACCESS.2024.3400693

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Abstract

En 中文

Within the e-commerce sphere, optimizing the product classification process assumes pivotal importance, owing to its direct influence on operational efficiency and profitability. In this context, employing machine learning algorithms stands out as a premier solution for effectively automating this process. The design of these models commonly adopts either a flat or local (hierarchical) approach. However, each of them exhibits significant limitations. The regional approach introduces taxonomic inconsistencies in predictions, whereas the flat approach becomes inefficient when dealing with extensive datasets featuring high granularity. Therefore, our research introduces a solution for hierarchical product classification based on a Machine Learning model that integrates flat and local (hierarchical) classification approaches using a 4-level electronic product dataset obtained from a renowned e-commerce platform in Latin America. In pursuit of this goal, a comparative analysis of seven machine learning algorithms, including Multinomial Naive Bayes, Linear Support Vector Classifier, Multinomial Logistic Regression, Random Forest, XGBoost, FastText, and Voting Ensemble, was conducted. This hybrid approach model performs better than models using a single approach. It surpassed the top-performing flat approach model by 0.15% and outperformed the leading local approach (Local Classifier per Level) model by 4.88%, as measured by the weighted F1-score. Additionally, this paper contributes to the academic community by presenting a significant Spanish-language dataset comprising over one million products and discussing the preprocessing techniques tailored for the dataset. It also addresses the study's inherent limitations and potential avenues for future exploration in this field.

Keywords:

Electronic commerce

Classification algorithms

Machine learning algorithms

Bayes methods

Support vector machines

Random forests

Performance evaluation

Data models

South America

Logistic regression

Inventory management

Product delivery

Machine learning

e-commerce

hierarchical product classification

local classifier per level

ensemble

A Flat-Hierarchical Approach Based on Machine Learning Model for e-Commerce Product Classification

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