Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

A web usage lattice based mining approach for intelligent web personalization

A web usage lattice based mining approach for intelligent web personalization With the explosive growth of information available on the World Wide Web, it has become much more difficult to access relevant information from the Web. One possible approach to solve this problem is web personalization. In this paper, we propose a novel WUL (Web Usage Lattice) based mining approach for mining association access pattern rules for personalized web recommendations. The proposed approach aims to mine a reduced set of effective association pattern rules for enhancing the online performance of web recommendations. We have incorporated the proposed approach into a personalized web recommender system known as AWARS. The performance of the proposed approach is evaluated based on the efficiency and the quality. In the efficiency evaluation, we measure the number of generated rules and the runtime for online recommendations. In the quality evaluation, we measure the quality of the recommendation service based on precision, satisfactory and applicability. This paper will discuss the proposed WUL‐based mining approach, and give the performance of the proposed approach in comparison with the Apriori‐based algorithms. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Web Information Systems Emerald Publishing

A web usage lattice based mining approach for intelligent web personalization

Loading next page...
 
/lp/emerald-publishing/a-web-usage-lattice-based-mining-approach-for-intelligent-web-Z153e0OFxb
Publisher
Emerald Publishing
Copyright
Copyright © 2005 Emerald Group Publishing Limited. All rights reserved.
ISSN
1744-0084
DOI
10.1108/17440080580000089
Publisher site
See Article on Publisher Site

Abstract

With the explosive growth of information available on the World Wide Web, it has become much more difficult to access relevant information from the Web. One possible approach to solve this problem is web personalization. In this paper, we propose a novel WUL (Web Usage Lattice) based mining approach for mining association access pattern rules for personalized web recommendations. The proposed approach aims to mine a reduced set of effective association pattern rules for enhancing the online performance of web recommendations. We have incorporated the proposed approach into a personalized web recommender system known as AWARS. The performance of the proposed approach is evaluated based on the efficiency and the quality. In the efficiency evaluation, we measure the number of generated rules and the runtime for online recommendations. In the quality evaluation, we measure the quality of the recommendation service based on precision, satisfactory and applicability. This paper will discuss the proposed WUL‐based mining approach, and give the performance of the proposed approach in comparison with the Apriori‐based algorithms.

Journal

International Journal of Web Information SystemsEmerald Publishing

Published: Aug 1, 2005

Keywords: Web usage mining; Web usage lattice; Association access pattern rules; Web recommendation

There are no references for this article.