Mining E-commerce Data from E-shop Websites

Horch, Andrea; Kett, Holger; Weisbecker, Anette

doi:10.1109/trustcom.2015.575

Cited by 12 publications

(3 citation statements)

References 7 publications

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“…These results have shown that 76% of the analysed websites had product lists on level 0, 96% presented product lists on level 1, 74% included lists of products on level 2 and still 12% had product lists on level 3. A more detailed description of our analysis is presented in [20]. After the crawling each Web page referenced by the collected URIs of the e-shop website is visited and checked for the occurrence of product records.…”

Section: Approachmentioning

confidence: 99%

“…The system assumes that the cluster which contains the largest number of elements includes the product records (see line 10 and 12). A more detailed description of LightExtractor is given in [20]- [21]. if element is not a StyleSheetElement: for parent in element.findParentNodes: if parent is not a StyleElement: element_path = "/" + parent.tagName + element_path try:…”

Section: Lightextractormentioning

confidence: 99%

“…The SimpleAttributeSnooper identifies and extracts the following product attributes from the product records which are extracted by LightExtractor: (1) product name, (2) product image, (3) product prices, (4) currency of prices and (5) link to the detail page of the product. The approach of the SimpleAttributeSnooper is presented in detail in [20], but we will give a short description of its function here, too. The approach assumes that the product image is included in the image element within the product record with the largest file size.…”

Section: Simpleattributesnoopermentioning

confidence: 99%

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A Web Application for the Collection and Comparison of E-Shop Product Data

2016

Proceedings of 2016 the 6th International Workshop on Computer Science and Engineering

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E-commerce is a highly competitive and constantly growing market. On-line retailers need to compare their product offers with those of their competitors in order to remain competitive. Due to the large range of product offers on the Web and the fact that internet prices are adjusted on a daily basis or even more frequently the manual comparison of on-line product offers is a very time consuming task. Therefore, on-line retailers prefer the use of automated tools for performing that work. There are many existing ready-to-use software tools and on-line services from price comparison websites to market intelligence tools, but those tools still require a greater effort for the configuration of the tool or the comparison of the offers. The contribution of this paper is a Web application to automatically identify, extract and compare the product offers of the own e-shop with those of defined competitors. The specialties of the presented Web application are its degree of automation and the little effort for configuration.

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Section: Approachmentioning

confidence: 99%

Section: Lightextractormentioning

confidence: 99%

Section: Simpleattributesnoopermentioning

confidence: 99%

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