From clickstreams to searchstreams: Search network graph evidence from a B2B e-market

Mingfeng Lin, Mei Lin, Robert J. Kauffman

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Consumers in e-commerce acquire information through search engines, yet to date there has been little empirical study on how users interact with the results produced by search engines. This is analogous to, but different from, the ever-expanding research on clickstreams, where users interact with static web pages. We propose a new network approach to analyzing search engine server log data. We call this searchstream data. We create graph representations based on the web pages that users traverse as they explore the search results that their use of search engines generates. We then analyze the graph-level properties of these search network graphs by conducting cluster analysis. We report preliminary evidence the presence of heterogeneity among users in terms of how they interact with search engines. This suggests that search engine users may not all benefit from the same functionality in the search engines they rely upon. We also offer additional evidence on the empirical regularities associated with a variety of relevant issues that arise in the business-to-business (B2B) e-market context that we have studied.

Original languageEnglish (US)
Title of host publicationICEC 2012 - 14th Annual International Conference on Electronic Commerce
Pages274-275
Number of pages2
DOIs
StatePublished - 2012
Event14th Annual International Conference on Electronic Commerce, ICEC 2012 - Singapore, Singapore
Duration: Aug 7 2012Aug 8 2012

Publication series

NameACM International Conference Proceeding Series

Other

Other14th Annual International Conference on Electronic Commerce, ICEC 2012
CountrySingapore
CitySingapore
Period8/7/128/8/12

Keywords

  • big data
  • clickstreams
  • data mining
  • graph theory
  • keyword search
  • online markets
  • search behavior
  • searchstreams

ASJC Scopus subject areas

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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