A web usage lattice based mining approach for intelligent web personalization

• Published in: International journal of Web information systems 2005-08, Vol.1 (3), p.137-146
• Main Authors: Zhou, Baoyao, Cheung Hui, Siu, Fong, Alvis C. M.
• Format: Article
• Language: English
• Subjects: Association access pattern rules; Web recommendation; Web usage lattice; Web usage mining
• ISSN: 1744-0084
• DOI: 10.1108/17440080580000089

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 WULbased mining approach, and give the performance of the proposed approach in comparison with the Aprioribased algorithms.