ClusteringE-CommerceSearch.PDFVIP

Clustering E-Commerce Search Engines based on Their Search Interface Pages Using WISE-Cluster Yiyao Lu, Hai He, Qian Peng, Weiyi Meng Department of Computer Science SUNY at Binghamton Binghamton, NY 13902, USA 1-607-777-4311 {ylu0, haihe, meng}@ Clement Yu Department of Computer Science University of Illinois at Chicago Chicago, IL 60607, USA 1-312-996-2318 yu@ 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. Keywords: E-commerce, search engine, clustering, categorization, Web-based information systems Corresponding Author: Weiyi Meng Department of Computer Science SUNY at Binghamton