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Extracting Product Offers from e-Shop Websites

  • Conference paper
Web Information Systems and Technologies (WEBIST 2015)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 246))

Abstract

On-line retailers as well as e-shoppers are very interested in gathering product records from the Web in order to compare products and prices. The consumers compare products and prices to find the best price for a specific product or they want to identify alternatives for a product whereas the on-line retailers need to compare their offers with those of their competitors for being able to remain competitive. As there is a huge number and vast array of product offers in the Web the product data needs to be collected through an automated approach. The contribution of this papers is a novel approach for automatically identify and extract product records from arbitrary e-shop websites. The approach extends an existing technique which is called Tag Path Clustering for clustering similar HTML tag paths. The clustering mechanism is combined with a novel filtering mechanism for identifying the product records to be extracted within the websites.

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Notes

  1. 1.

    http://open.dapper.net/.

  2. 2.

    https://www.kimonolabs.com/.

  3. 3.

    https://import.io/.

  4. 4.

    http://www.diffbot.com/products/crawlbot/.

  5. 5.

    http://www.webkit.org/.

  6. 6.

    http://mathworld.wolfram.com/PrimeSpiral.html.

  7. 7.

    http://www.bestarabic.com/mall/ar/.

  8. 8.

    http://docs.seleniumhq.org/projects/webdriver/.

  9. 9.

    https://www.python.org/.

  10. 10.

    http://selenium-python.readthedocs.org/en/latest/api.html.

  11. 11.

    http://www.crummy.com/software/BeautifulSoup/.

  12. 12.

    http://www.cs.uic.edu/~liub/WebDataExtraction/MDR-download.html.

  13. 13.

    http://clustvx.no-ip.org/.

  14. 14.

    https://www.python.org/.

  15. 15.

    http://selenium-python.readthedocs.org/en/latest/api.html.

  16. 16.

    http://www.crummy.com/software/BeautifulSoup/.

  17. 17.

    http://www.cs.uic.edu/~liub/WebDataExtraction/MDR-download.html.

  18. 18.

    http://clustvx.no-ip.org/.

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Acknowledgements

The work published in this article was partially funded by the SME E-COMPASS project of the European Union’s Seventh Framework Programme for research, technological development and demonstration under the grant agreement no. 315637.

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Authors and Affiliations

  1. Fraunhofer Institute for Industrial Engineering IAO, Stuttgart, Germany

    Andrea Horch, Holger Kett & Anette Weisbecker

Authors
  1. Andrea Horch
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  2. Holger Kett
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  3. Anette Weisbecker
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Corresponding author

Correspondence to Andrea Horch .

Editor information

Editors and Affiliations

  1. University of Paris, Paris, France

    Valérie Monfort

  2. RWTH Aachen University, Aachen, Germany

    Karl-Heinz Krempels

  3. Department of Information Systems, University of Agder, Kristiansand, Vest-Agder Fylke, Norway

    Tim A. Majchrzak

  4. Faculty of Civil and Geodetic Engineering, University of Ljubljana, Ljubljana, Slovenia

    Žiga Turk

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Horch, A., Kett, H., Weisbecker, A. (2016). Extracting Product Offers from e-Shop Websites. In: Monfort, V., Krempels, KH., Majchrzak, T.A., Turk, Ž. (eds) Web Information Systems and Technologies. WEBIST 2015. Lecture Notes in Business Information Processing, vol 246. Springer, Cham. https://doi.org/10.1007/978-3-319-30996-5_12

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  • DOI: https://doi.org/10.1007/978-3-319-30996-5_12

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-30995-8

  • Online ISBN: 978-3-319-30996-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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