Billion-scale Pre-trained E-commerce Product Knowledge Graph Model

Zhang, Wen; Wong, Chi-Man; Ye, Ganqiang; Wen, Bo; Zhang, Wei; Chen, Huajun

doi:10.1109/icde51399.2021.00280

Cited by 13 publications

(5 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Considering only textual product attributes, as is the case of this research, it is common to work with but not limited to product title, description, price, brand, and breadcrumbs. Nevertheless, the majority tend to work with the product title and/or description [4], [8], [11], [18], [22]. The following paragraphs describe several pieces of research that utilize textual product attributes as their study object.…”

Section: Related Workmentioning

confidence: 99%

“…However, the challenges of achieving efficient hierarchical product classification automation lie in the vast volume of e-commerce data [5], the ambiguity in product descriptions, data imbalance [6], multilingualism [7], [8], and scalability [9]. Recent advancements in machine learning, along with the continuous efforts of numerous authors, provide tools to address these challenges.…”

Section: Introductionmentioning

confidence: 99%

“…Recent advancements in machine learning, along with the continuous efforts of numerous authors, provide tools to address these challenges. These tools span from the introduction of new datasets [3], [10] to the development of transformer-based models [8], [11] and the exploration of multimodal approaches [12], [13].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Cotacallapa,

Saboya,

Canas Rodrigues

et al. 2024

IEEE Access

View full text Add to dashboard Cite

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 local 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 both 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 exhibits superior performance compared to 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 F1score. Additionally, this paper contributes to the academic community by presenting a significant Spanishlanguage dataset comprising over one million products and discussing the optimal preprocessing techniques tailored for the dataset. It also addresses the study's inherent limitations and potential avenues for future exploration in this field. INDEX TERMS machine learning, e-commerce, hierarchical product classification, local classifier per level, ensemble

show abstract

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Cotacallapa,

Saboya,

Canas Rodrigues

et al. 2024

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Furthermore, entityrelation triples, or the Structure Contextual Triples called in Ref. [28], are extracted from the paragraphs and linked to the sections or subsections they belong to, and then we get the metaknowledge of Ref. [27].…”

Section: Generating Metaknowldgementioning

confidence: 99%

Metaknowledge Extraction Based on Multi-Modal Documents

Liu

Sun

et al. 2021

Preprint

View full text Add to dashboard Cite

The triple-based knowledge in large-scale knowledge bases is most likely lacking in structural logic and problematic of conducting knowledge hierarchy. In this paper, we introduce the concept of metaknowledge to knowledge engineering research for the purpose of structural knowledge construction. Therefore, the Metaknowledge Extraction Framework and Document Structure Tree model are presented to extract and organize metaknowledge elements (titles, authors, abstracts, sections, paragraphs, etc.), so that it is feasible to extract the structural knowledge from multi-modal documents. Experiment results have proved the effectiveness of metaknowledge elements extraction by our framework. Meanwhile, detailed examples are given to demonstrate what exactly metaknowledge is and how to generate it. At the end of this paper, we propose and analyze the task flow of metaknowledge applications and the associations between knowledge and metaknowledge.

show abstract

“…In this paper, we report our experiences on e-commerce knowledge graph application. Our e-commerce knowledge graph [51] is built as a union way to integrate massive information about items, online-shops, users, etc., on the e-commerce platform. It help us build the integrated view of data and contribute to a variety of business tasks ranging from searching, question answering, recommendation [42] and business intelligence, etc.…”

Section: Introductionmentioning

confidence: 99%