文章基本信息

标题：A WEB USAGE ASSOCIATION LEARNING METHODS BASED ON MINING TECHNIQUES FOR WEB PERSONALIZATION
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作者：Y.RAJU ; DR D. SURESH BABU ; DR.K.ANURADHA 等
期刊名称：Journal of Theoretical and Applied Information Technology
印刷版ISSN：1992-8645
电子版ISSN：1817-3195
出版年度：2018
卷号：96
期号：10
出版社：Journal of Theoretical and Applied
摘要：The increase in information resources on the World Wide Web allows users to find the information they need and navigate through multiple sites on the web. Because the web is huge and complex, users often unable to reach the lookout page when surfing the web. Web personalization is one of the potential conducts to solve this problem by leveraging the knowledge gained from the analysis of users accessing activities in the web usage logs to adapt the content and structure of the website to our needs. The existing approach focuses more on building user profiles that rely on web pages or documents that affect the effectiveness of web personalization. In this paper, we propose a web usage association (WUA) learning methods based on log usage association learning and personalized cluster mining technique for effective web personalization. The proposed method classifies the data using "frequent pattern mining (FPM)" and "Multi-Stage Association Rules (MAR) for the user's interest in navigation sites and personalization, and the chronic relationship of web usage using hierarchical methods and clustering. The Experimental evaluation has shown that the proposed approach has achieved effective personalization precision measurements for user interest and can be used in real-time personalization systems to minimize the storage cost and provide the provisioning for resources personalization in real time systems.
关键词：Web Usage; Association Learning; Log usage association learning; personalized cluster; Personalization.