Title :
An unsupervised method for information retrieval on E-commerce websites
Author :
Baoqiu Wang ; Yukun Zhong
Author_Institution :
Dept. of Comput. Sci. & Eng., Sichuan Univ. Jinjiang Coll., Pengshan, China
Abstract :
In this paper, we present an unsupervised method to find the covert properties of the product on E-commerce web sites. Our method is generic because we do not have to depend on web site restricted pattern. The method works by 3 algorithms which are Page Type Recognition, List Page Clustering and Query Relationship Discovery. Experimental results show that it can acquire accurate rate of 96% and recall rate of 94%.
Keywords :
Web sites; electronic commerce; query processing; unsupervised learning; e-commerce Websites; information retrieval; list page clustering; page type recognition; query relationship discovery; unsupervised method; Classification algorithms; Clustering algorithms; Electronic commerce; Time complexity; Web pages; covert properties; e-commerce websites; information retrieval;
Conference_Titel :
Information Technology and Electronic Commerce (ICITEC), 2014 2nd International Conference on
Print_ISBN :
978-1-4799-5298-4
DOI :
10.1109/ICITEC.2014.7105580
