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Social Networks as Data Source for Recommendation Systems

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E-Commerce and Web Technologies (EC-Web 2010)

Part of the book series: Lecture Notes in Business Information Processing ((LNBIP,volume 61))

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Abstract

Reviews and review based rankings are widely used in recommendation systems to provide potential customers quality information about selected products. During the last years, many researchers have shown that these reviews are neither objective nor do they represent real quality values. Even established ranking methods designed to fix this problem have been shown to be unreliable. In this work, user generated content of fora, weblogs and similar trustworthy social networks is proposed as an alternative data source. It is shown how this data can be used to calculate a satisfaction and relevance measure for different product features to provide potential customers reliable quality information. The method is evaluated in the automotive domain using J.D. Power’s established Initial Quality Study to ensure providing meaningful quality-related data.

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Author information

Authors and Affiliations

  1. Daimler AG, Research & Development, D-89081, Ulm, Germany

    Mathias Bank & Juergen Franke

Authors
  1. Mathias Bank
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  2. Juergen Franke
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Editor information

Editors and Affiliations

  1. DIMET Dept., Università degli Studi Mediterranea di Reggio Calabria, via Graziella, loc. Feo di Vito, 89122, Reggio Calabria, Italy

    Francesco Buccafurri

  2. Dipartimento di Informatica, Università degli Studi di Bari, Via E. Orabona, 4, 70125, Bari, Italy

    Giovanni Semeraro

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Bank, M., Franke, J. (2010). Social Networks as Data Source for Recommendation Systems. In: Buccafurri, F., Semeraro, G. (eds) E-Commerce and Web Technologies. EC-Web 2010. Lecture Notes in Business Information Processing, vol 61. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-15208-5_5

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  • DOI: https://doi.org/10.1007/978-3-642-15208-5_5

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-15207-8

  • Online ISBN: 978-3-642-15208-5

  • eBook Packages: Computer ScienceComputer Science (R0)

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