Self-adjusting binary search trees
Journal of the ACM (JACM)
Min-max heaps and generalized priority queues
Communications of the ACM
Enabling scalable online personalization on the Web
Proceedings of the 2nd ACM conference on Electronic commerce
Web mining for web personalization
ACM Transactions on Internet Technology (TOIT)
Web Reconfiguration by Spatio-Temporal Page Personalization Rules Based on Access Histories
SAINT '01 Proceedings of the 2001 Symposium on Applications and the Internet (SAINT 2001)
Memory access analysis and optimization approaches on splay trees
LCR '04 Proceedings of the 7th workshop on Workshop on languages, compilers, and run-time support for scalable systems
ICDE '06 Proceedings of the 22nd International Conference on Data Engineering
Dynamic personalization of web sites without user intervention
Communications of the ACM - Spam and the ongoing battle for the inbox
Web site personalization based on link analysis and navigational patterns
ACM Transactions on Internet Technology (TOIT)
Genre Categorization of Web Pages
ICDMW '07 Proceedings of the Seventh IEEE International Conference on Data Mining Workshops
Computational Intelligence techniques for Web personalization
Web Intelligence and Agent Systems
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The explosive growth in the size and use of the World Wide Web continuously creates new great challenges and needs. The need for predicting the users' preferences in order to expedite and improve the browsing though a site can be achieved through personalizing of the Websites. Recommendation and personalization algorithms aim at suggesting WebPages to users based on their current visit and past users' navigational patterns. The problem that we address is the case where few WebPages become very popular for short periods of time and are accessed very frequently in a limited temporal space. Our aim is to deal with these bursts of visits and suggest these highly accessed pages to the future users that have common interests. Hence, in this paper, we propose a new web personalization technique, based on advanced data structures. The data structures that are used are the Splay tree (1) and Binary heaps (2). We describe the architecture of the technique, analyze the time and space complexity and prove its performance. In addition, we compare both theoretically and experimentally the proposed technique to another approach to verify its efficiency. Our solution achieves O(P^2) space complexity and runs in klogP time, where k is the number of pages and P the number of categories of WebPages.