A Dataset and Baselines for e-Commerce Product Categorization

Lin, Yiu-Chang; Das, Pradipto; Trotman, Andrew; Kallumadi, Surya

doi:10.1145/3341981.3344237

Cited by 6 publications

(3 citation statements)

References 6 publications

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“…The development of automatic product categorization systems for e-commerce and online marketplace platforms has gained significant attention in recent years. Researchers and practitioners alike have explored various methodologies and techniques to tackle the challenges associated with this complex task [1].…”

Section: Literature Surveymentioning

confidence: 99%

Design and Development of an Automated Product Categorization Software: AI-Driven Solutions for E-Commerce Platforms

Oskooei,

Terim,

Arık

et al. 2023

OPRD

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This article outlines the design and development process of an automatic product categorization software intended for use in e-commerce and online marketplace platforms. The project aims to tackle the urgent issue of effectively classifying a wide range of products within digital markets. By utilizing artificial intelligence (AI) and machine learning methodologies, the software effectively analyzes product descriptions, enabling users to seamlessly incorporate products through automated categorization. The significance of the project is rooted in its ability to guarantee the accuracy and consistency of category hierarchies, automate the process of categorizing, and improve the overall user experience. The main goals involve the process of project planning, data collecting, and preparation. Machine learning models have been built and subsequently incorporated to facilitate the study of product descriptions. Through strict evaluation and optimization processes, a high level of accuracy and efficiency is achieved, resulting in several anticipated benefits. These benefits cover automated product categorization, enhanced user experience, and the potential for online platforms to gain a competitive edge. The key elements of innovation involve AI-driven textual analysis, learning methodologies grounded in data, and the ability to adapt to diverse industry contexts. Precautions and backup strategies are implemented to tackle technical issues, including the selection of machine learning libraries and algorithms, ensuring data quality, and integrating with various platforms. The success criteria encompass the objective of achieving a minimum prediction accuracy rate of 90%, optimizing business efficiency, enhancing user pleasure, and ensuring smooth system functioning. This project is a significant contribution to the field of product categorization inside the digital marketplace, as it provides automation, accuracy, and efficiency, ultimately resulting in an enhanced user experience.

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Section: Literature Surveymentioning

confidence: 99%

Design and Development of an Automated Product Categorization Software: AI-Driven Solutions for E-Commerce Platforms

Oskooei,

Terim,

Arık

et al. 2023

OPRD

View full text Add to dashboard Cite

show abstract

“…Amazon Marketplace products are presented on detailed product pages and, as on other e-commerce websites [7][8][9], are arranged in a taxonomy of categories to favor consistent navigation structures, thereby enhancing the user experience and the website's usability. Each detailed product page provides a proper description of the product, which includes its characteristics, consumer reviews, stock availability, the price or product rating, and the Buy Box .…”

Section: Introductionmentioning

confidence: 99%

Automatic Eligibility of Sellers in an Online Marketplace: A Case Study of Amazon Algorithm

2022

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Purchase processes on Amazon Marketplace begin at the Buy Box, which represents the buy click process through which numerous sellers compete. This study aimed to estimate empirically the relevant seller characteristics that Amazon could consider featuring in the Buy Box. To that end, 22 product categories from Italy’s Amazon web page were studied over a ten-month period, and the sellers were analyzed through their products featured in the Buy Box. Two different experiments were proposed and the results were analyzed using four classification algorithms (a neural network, random forest, support vector machine, and C5.0 decision trees) and a rule-based classification. The first experiment aimed to characterize sellers unspecifically by predicting their change at the Buy Box. The second one aimed to predict which seller would be featured in it. Both experiments revealed that the customer experience and the dynamics of the sellers’ prices were important features of the Buy Box. Additionally, we proposed a set of default features that Amazon could consider when no information about sellers was available. We also proposed the possible existence of a relationship or composition among important features that could be used for sellers to be featured in the Buy Box.

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“…Figure 2 In this study, we proposed data acquired from Shopee E-commerce Thailand between October and December 2021. Researchers gathered information from E-commerce platforms for research purposes [76][77][78][79][80]. Shopee also provides data with the Shopee Open Platform [81] for developers to create Partner Apps that expand vendors' businesses.…”

mentioning

confidence: 99%

A Semi-Supervised Learning Approach for Automatic Detection and Fashion Product Category Prediction with Small Training Dataset Using FC-YOLOv4

2022

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Over the past few decades, research on object detection has developed rapidly, one of which can be seen in the fashion industry. Fast and accurate detection of an E-commerce fashion product is crucial to choosing the appropriate category. Nowadays, both new and second-hand clothing is provided by E-commerce sites for purchase. Therefore, when categorizing fashion clothing, it is essential to categorize it precisely, regardless of the cluttered background. We present recently acquired tiny product images with various resolutions, sizes, and positions datasets from the Shopee E-commerce (Thailand) website. This paper also proposes the Fashion Category—You Only Look Once version 4 model called FC-YOLOv4 for detecting multiclass fashion products. We used the semi-supervised learning approach to reduce image labeling time, and the number of resulting images is then increased through image augmentation. This approach results in reasonable Average Precision (AP), Mean Average Precision (mAP), True or False Positive (TP/FP), Recall, Intersection over Union (IoU), and reliable object detection. According to experimental findings, our model increases the mAP by 0.07 percent and 40.2 percent increment compared to the original YOLOv4 and YOLOv3. Experimental findings from our FC-YOLOv4 model demonstrate that it can effectively provide accurate fashion category detection for properly captured and clutter images compared to the YOLOv4 and YOLOv3 models.

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A Dataset and Baselines for e-Commerce Product Categorization

Cited by 6 publications

References 6 publications

Design and Development of an Automated Product Categorization Software: AI-Driven Solutions for E-Commerce Platforms

Design and Development of an Automated Product Categorization Software: AI-Driven Solutions for E-Commerce Platforms

Automatic Eligibility of Sellers in an Online Marketplace: A Case Study of Amazon Algorithm

A Semi-Supervised Learning Approach for Automatic Detection and Fashion Product Category Prediction with Small Training Dataset Using FC-YOLOv4

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