A fast method for discovering critical edge sequences in e-commerce catalogs |
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| Authors: | Kaushik Dutta Debra VanderMeer Anindya Datta Pinar Keskinocak Krithi Ramamritham |
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| Affiliation: | 1. Department of Decision Sciences and Information Systems, Florida International University, Miami, Florida 33199, United States;2. Walking Stick Solutions, 75 Fifth Street NW, Suite 218, Atlanta, Georgia 30308, United States;3. School of Information Systems, Singapore Management University, 80 Stamford Rd, Singapore 178902;4. School of Industrial and Systems Engineering, Georgia Institute of Technology, Atlanta, Georgia 30332, United States;5. Department of Computer Science and Engineering, Indian Institute of Technology Bombay, Powai, Mumbai 400076, India |
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| Abstract: | Web sites allow the collection of vast amounts of navigational data – clickstreams of user traversals through the site. These massive data stores offer the tantalizing possibility of uncovering interesting patterns within the dataset. For e-businesses, always looking for an edge in the hyper-competitive online marketplace, the discovery of critical edge sequences (CESs), which denote frequently traversed sequences in the catalog, is of significant interest. CESs can be used to improve site performance and site management, increase the effectiveness of advertising on the site, and gather additional knowledge of customer behavior patterns on the site. |
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| Keywords: | Data mining e-Commerce Graph theory Applied probability |
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