A Semantic Web Personalizing Technique: 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. A personalization mechanism is based on explicit preference declarations by the user and on an iterative process of monitoring the user navigation, collecting its requests of ontological objects and storing them in its profile in order to deliver personalized content. The problem that we address is the case where few web pages 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 k·logP time, where k is the number of pages and P the number of ontologies of Web pages.

I. Introduction

The Semantic Web leverages the knowledge integration on the Web to new levels. Despite the efforts put into the technical and research issues, there are few applications actually deploying and evaluating semantic web with real users. Semantic web can only deliver if it is driven by user needs, context or profiles to seamlessly integrate the knowledge on the web to really provide desirable content.