文章基本信息

标题：Finding of Weighted Sequential Web Access Patterns for Effective Web Page Recommendations
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作者：K. Suneetha ; Dr. M. Usha Rani
期刊名称：International Journal of Computer Science & Technology
印刷版ISSN：2229-4333
电子版ISSN：0976-8491
出版年度：2012
卷号：3
期号：3
页码：884-888
语种：English
出版社：Ayushmaan Technologies
摘要：Recommender systems aim at directing users through this information space, toward the resources that best meet their needs and interests by extracting knowledge from the previous users’ interactions. Currently much research is focus on web page recommendations using sequential pattern mining techniques. Sequential access pattern mining discovers interesting and frequent user access patterns from web logs. Most of the previous studies have adopted Apriori-like sequential pattern mining techniques, which faced the problem on requiring expensive multiple scans of databases. In this paper a traditional sequential pattern mining algorithm called prefixspan is modified by incorporating two measures such as, spending time and recent view. Then, the weighted sequential patterns are utilized to construct the recommendation model using the Patricia trie-based tree structure. Finally, the recommendation of the current users is done with the help of markov model.
关键词：Pattern Growth Approach;Frequent Patterns;Projection Database; Web Page Recommendations;Particia-Trie