A web personalizing technique using adaptive data structures: The case of bursts in web visits
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(P2) space complexity and runs in klogP time, where k is the number of pages and P the number of categories of WebPages.
web personalization adaptive data structures burst web visits