A Flat-Hierarchical Approach Based on Machine Learning Model for e-Commerce Product Classification
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 p...
Guardado en:
| Autores principales: | , , , , |
|---|---|
| Formato: | Artículo |
| Lenguaje: | inglés |
| Publicado: |
2024
|
| Acceso en línea: | Descargar el archivo Enlace permanente (DOI o Handle) Enlace permanente (DOI o Handle) |
| Etiquetas: |
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1881508829351378944 |
|---|---|
| author | Cotacallapa, Harold Saboya Rios, Nemias Canas Rodrigues, Paulo Salas, Rodrigo López Gonzales, Javier Linkolk |
| author_browse | Canas Rodrigues, Paulo Cotacallapa, Harold López Gonzales, Javier Linkolk Saboya Rios, Nemias Salas, Rodrigo |
| author_facet | Cotacallapa, Harold Saboya Rios, Nemias Canas Rodrigues, Paulo Salas, Rodrigo López Gonzales, Javier Linkolk |
| author_sort | Cotacallapa, Harold |
| collection | Producción científica |
| description | 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. |
| format | Article |
| id | oai:cris.upeu.edu.pe:20.500.15037/423 |
| institution | UPeU |
| language | English |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| record_format | dspace |
| spelling | oai:cris.upeu.edu.pe:20.500.15037/4232026-11-02T22:00:04ZA Flat-Hierarchical Approach Based on Machine Learning Model for e-Commerce Product ClassificationCotacallapa, HaroldSaboya Rios, NemiasCanas Rodrigues, PauloSalas, RodrigoLópez Gonzales, Javier LinkolkWithin 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.Articlehttps://ieeexplore.ieee.org/ielx7/6287639/6514899/10530036.pdfhttp://dx.doi.org/10.1109/access.2024.340069310.1109/access.2024.3400693https://hdl.handle.net/20.500.15037/423W4396852936enIEEE Access2169-35362024-01-012026-06-13T08:13:15Z2026-06-13T08:13:15Z |
| spellingShingle | Cotacallapa, Harold Saboya Rios, Nemias Canas Rodrigues, Paulo Salas, Rodrigo López Gonzales, Javier Linkolk A Flat-Hierarchical Approach Based on Machine Learning Model for e-Commerce Product Classification |
| title | A Flat-Hierarchical Approach Based on Machine Learning Model for e-Commerce Product Classification |
| title_full | A Flat-Hierarchical Approach Based on Machine Learning Model for e-Commerce Product Classification |
| title_fullStr | A Flat-Hierarchical Approach Based on Machine Learning Model for e-Commerce Product Classification |
| title_full_unstemmed | A Flat-Hierarchical Approach Based on Machine Learning Model for e-Commerce Product Classification |
| title_short | A Flat-Hierarchical Approach Based on Machine Learning Model for e-Commerce Product Classification |
| title_sort | flat hierarchical approach based on machine learning model for e commerce product classification |
| url | https://ieeexplore.ieee.org/ielx7/6287639/6514899/10530036.pdf http://dx.doi.org/10.1109/access.2024.3400693 https://hdl.handle.net/20.500.15037/423 |
| work_keys_str_mv | AT cotacallapaharold aflathierarchicalapproachbasedonmachinelearningmodelforecommerceproductclassification AT saboyariosnemias aflathierarchicalapproachbasedonmachinelearningmodelforecommerceproductclassification AT canasrodriguespaulo aflathierarchicalapproachbasedonmachinelearningmodelforecommerceproductclassification AT salasrodrigo aflathierarchicalapproachbasedonmachinelearningmodelforecommerceproductclassification AT lopezgonzalesjavierlinkolk aflathierarchicalapproachbasedonmachinelearningmodelforecommerceproductclassification AT cotacallapaharold flathierarchicalapproachbasedonmachinelearningmodelforecommerceproductclassification AT saboyariosnemias flathierarchicalapproachbasedonmachinelearningmodelforecommerceproductclassification AT canasrodriguespaulo flathierarchicalapproachbasedonmachinelearningmodelforecommerceproductclassification AT salasrodrigo flathierarchicalapproachbasedonmachinelearningmodelforecommerceproductclassification AT lopezgonzalesjavierlinkolk flathierarchicalapproachbasedonmachinelearningmodelforecommerceproductclassification |