Automated product taxonomy mapping in an e-commerce environment

Aanen, Steven S.; Vandic, Damir; Frăsincar, Flavius

doi:10.1016/j.eswa.2014.09.032

Cited by 23 publications

(13 citation statements)

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“…Early studies on automatic product classification exploited the traditional text classifiers such as Naïve Bayes, SVM (support vector machine), and KNN (k-nearest neighbors) [13][14][15]. Aanen, S. et al [16] exploited a lexical similarity to improve the disambiguation of product taxonomy and suggested an ontology-based approach using WordNet. Lee, T., et al [17] also suggested an ontological approach to classify the products, and they exploited a set of ontological data dictionaries for keyword-based product classification.…”

Section: Related Workmentioning

confidence: 99%

Developing Data-Conscious Deep Learning Models for Product Classification

2021

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In online commerce systems that trade in many products, it is important to classify the products accurately according to the product description. As may be expected, the recent advances in deep learning technologies have been applied to automatic product classification. The efficiency of a deep learning model depends on the training data and the appropriateness of the learning model for the data domain. This is also applicable to deep learning models for automatic product classification. In this study, we propose deep learning models that are conscious of input data comprising text-based product information. Our approaches exploit two well-known deep learning models and integrate them with the processes of input data selection, transformation, and filtering. We demonstrate the practicality of these models through experiments using actual product information data. The experimental results show that the models that systematically consider the input data may differ in accuracy by approximately 30% from those that do not. This study indicates that input data should be sufficiently considered in the development of deep learning models for product classification.

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Section: Related Workmentioning

confidence: 99%

Developing Data-Conscious Deep Learning Models for Product Classification

2021

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“…From 2005 to 2013, China's cross-border e-commerce has achieved vigorous development. In addition to the financial crisis in 2009, it has achieved substantial growth in other years, earning huge foreign exchange for China (Aanen et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Research and Analysis of an Enterprise E-Commerce Marketing System Under the Big Data Environment

Zhang

2021

Journal of Organizational and End User Computing

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As an emerging online shopping method, e-commerce has been widely popular since the popularization of the Internet. Online sales and online shopping have become the trend of modern business development. However, the functionality and performance conditions of the existing platform cannot be closely integrated with the merchant's own business. The purpose of this paper is to study the enterprise e-commerce marketing system based on big data. The system design of this paper adopts SSH framework as the main technology, the database selects HBase database, and the front end combines with Web2.0 technology for the interaction of interface display and operation. The experimental results show that applying big data technology to enterprise e-commerce marketing system has extremely important practical significance. Perform a performance analysis on this system,when the amount of data reaches 4000, the speed of HBase is 10.486s, and the query time of Mysql is 50.184s. It can be seen that the Hbase database query speed is much faster than the Mysql database query speed.

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“…A taxonomy is a semantic hierarchy that organizes concepts by is-a relations, which exhibits the capability of improving many NLP and IR tasks, such as query understanding (Hua et al, 2017), personalized recommendation , question answering (Yang et al, 2017), etc. It also supports a variety of real-world applications, including information management (Nickerson et al, 2013), e-commerce (Aanen et al, 2015) and biomedical systems (Köhler et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

“…It also supports a variety of real-world applications, including information management (Nickerson et al, 2013), e-commerce (Aanen et al, 2015) and biomedical systems (Köhler et al, 2014).…”

Section: Introductionmentioning

confidence: 99%

A Short Survey on Taxonomy Learning from Text Corpora: Issues, Resources and Recent Advances

Wang¹,

He²,

Zhou³

2017

Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

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A taxonomy is a semantic hierarchy, consisting of concepts linked by is-a relations. While a large number of taxonomies have been constructed from human-compiled resources (e.g., Wikipedia), learning taxonomies from text corpora has received a growing interest and is essential for longtailed and domain-specific knowledge acquisition. In this paper, we overview recent advances on taxonomy construction from free texts, reorganizing relevant subtasks into a complete framework. We also overview resources for evaluation and discuss challenges for future research.

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Automated product taxonomy mapping in an e-commerce environment

Cited by 23 publications

References 50 publications

Developing Data-Conscious Deep Learning Models for Product Classification

Developing Data-Conscious Deep Learning Models for Product Classification

Research and Analysis of an Enterprise E-Commerce Marketing System Under the Big Data Environment

A Short Survey on Taxonomy Learning from Text Corpora: Issues, Resources and Recent Advances

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