Anonymous market product classification based on deep learning

Yang, Lina; Yang, Ying; Yu, Huanhuan; Zhu, Guichun

doi:10.1145/3371425.3371467

Cited by 2 publications

(2 citation statements)

References 7 publications

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“…There are two data sources used in previous studies. First, they used confidential data that were extracted from e-commerce websites or permissions to use data extracted by third party companies [8][9][10][11][12][13][14][15][16][17][18]. Studies that used this kind of data extraction intended to secure their data from being wrongly used by others.…”

Section: Data Extractionmentioning

confidence: 99%

“…These researchers used various partition ratios that randomly split the data into approximately 70% and 30% for training and testing parts, respectively [68]. Some researchers mention that the validation part is important to avoid model overfitting [2,3,8,11,17,18,22]. It is necessary to have a validation data set as well as the training and test data sets when adjusting any classification parameter [69].…”

Section: Data Partitionmentioning

confidence: 99%

See 1 more Smart Citation

A Mini Review on Supervised Learning Models for E-Commerce Product Classification

2020

IJATCSE

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Nowadays, consumers use e-commerce websites to find their desired products. Some researchers and related services collect data from these websites for research purposes. The misclassification of the product may distract them from finding the right products. Thus, product classification is essential in e-commerce websites. Machine learning models are widely used to classify the products according to their categories. This review presents the supervised learning model and its applications in this field. Various articles were analysed on the use of different supervised learning models for product classification. The review provides different types of supervised learning models and its enhancement in dealing with e-commerce products data. The findings are crucial for other researchers and practitioners that can be used to improve their current supervised learning model for e-commerce product classification.

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Section: Data Extractionmentioning

confidence: 99%

Section: Data Partitionmentioning

confidence: 99%

A Mini Review on Supervised Learning Models for E-Commerce Product Classification

2020

IJATCSE

View full text Add to dashboard Cite

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Content Labelling of Hidden Services With Keyword Extraction Using the Graph Decomposition Method

Dalvi¹,

Raut²,

Joshi³

et al. 2022

Advances in Digital Crime, Forensics, and Cyber Terrorism

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The data investigation of hidden services on the dark web is gaining attention from the research community and law enforcement agencies. However, the anonymity feature of hidden services makes it difficult to index the hidden services for investigation. Therefore, one of the primary focuses of dark web data investigation research is labelling the hidden services so that the labelled services can be classified or indexed further. The methodology deployed in the proposed work is based on keyword extraction using the graph degeneracy method. The proposed work analyzes the text data by extracting keywords from each hidden service document. The accuracy of the proposed method is validated by LDA-based topic modelling approach. The document labelling obtained by the keyword extraction method and LDA model matched with the accuracy of 78. The main intuition behind the keywords extraction method is that central nodes make good keywords. This is because central nodes with high centrality in the GoW of a document correspond to the document's keywords, which are well-understood by humans.

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Anonymous market product classification based on deep learning

Cited by 2 publications

References 7 publications

A Mini Review on Supervised Learning Models for E-Commerce Product Classification

A Mini Review on Supervised Learning Models for E-Commerce Product Classification

Content Labelling of Hidden Services With Keyword Extraction Using the Graph Decomposition Method

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