Clustering e-commerce search engines based on their search interface pages using WISE-cluster

  • Authors:
  • Yiyao Lu;Hai He;Qian Peng;Weiyi Meng;Clement Yu

  • Affiliations:
  • Department of Computer Science, SUNY at Binghamton, Binghamton, NY;Department of Computer Science, SUNY at Binghamton, Binghamton, NY;Department of Computer Science, SUNY at Binghamton, Binghamton, NY;Department of Computer Science, SUNY at Binghamton, Binghamton, NY;Department of Computer Science, University of Illinois at Chicago, Chicago, IL

  • Venue:
  • Data & Knowledge Engineering - Special issue: WIDM 2004
  • Year:
  • 2006

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Abstract

In this paper, we propose a new approach to clustering e-commerce search engines (ESEs) on the Web. Our approach utilizes the features available on the interface page of each ESE, including the label terms and value terms appearing in the search form, the number of images, normalized price terms as well as other terms. The experimental results based on more than 400 ESEs indicate that the proposed approach has good clustering accuracy. The importance of different types of features is analyzed and the terms in the search form are the most important feature in obtaining quality clusters.