Markov models have been widely used for modelling users' navigational behaviour in the Web graph, using the transitional probabilities between web pages, as recorded in the web logs. The recorded users ' navigation is used to extract popular web paths and predict current users ’ next steps. Such purely usage-based probabilistic models, however, present certain shortcomings. Since the prediction of users ' navigational behaviour is based solely on the usage data, structural properties of the Web graph are ignored. Thus important- in terms of pagerank authority score- paths may be underrated. In this paper we present a hybrid probabilistic predictive model extending the properties of Markov models by incorporating link analysis methods. More specifically, we propose the use of a PageRank-style algorithm for assigning prior probabilities to the web pages based on their importance in the web site's graph. We prove, through experimentation, that this approach results in more objective and representative predictions than the ones produced from the pure usage-based approaches.

Abstract—In this paper we will report the work currently being doing one the Gugubarra project. The project aims at building tools for better management of communities of Web visitors.

In [7] we have introduced the concept of non-obvious user profiles (NOPs) to capture the hypothetical interest of web users. In this paper we present the design principles and rules of our Gugubarra engine, which is a tool to calculate and visualize these non-obvious user profiles. 1.

Building profiles of registered users of a web site, as in case of a portal, is of importance if it goes beyond collecting the obvious information the user is willing to give at the time of registration. The starting point of this work is the assumption that a community of users is registered on a web site and that for each user a profile is built. Such a profile contains two parts of data: one obvious, given directly by the user and one less obvious, inferred by the user’s behavior during his visits on the site. This paper presents ideas on how to build a user profile based on non-obvious information and takes into account various factors influencing in its development. Special emphases are paid on feedback mechanism and several methods of measuring its results. Keywords: Non-obvious user profile, Web page ranking, Feedback mechanism 1.

Abstract-In this paper a method for detecting Denial-of-Service attacks in Web sites is presented. The detection of Web attacks are distinguished from normal user patterns by inserting decoy hyperlinks into some key pages in the Website. Typical types of decoy hyperlinks are described and experimental results derived from real Web-sites gives the extremely low false positive rate of 0.0421%. A method for selecting an effective and minimal number of decoy hyperlinks and pages is also presented and evaluated in real and simulated data.

In this paper, we present a long-term study of user-centric Web traffic data collected in 2000-2002 and 2005-2006 from two large representative panels of French Internet users. Our work focuses on the dynamics of personal territories on the Web and their evolution between 2000 and 2006. At the session level, we distinguish four profiles of browsing dynamics in 2005-2006, and point out the growing dichotomy between straight routine sessions and exploratory browsing. At a global level, we observe that although each individual’s corpus of visited sites is permanently growing, his browsing practices are structured around routine well-known sites which operate as links providers to new sites. We argue that this tension between the known and the unknown is constitutive of Web practices and is a fundamental property of personal Web territories.

In this paper, we report our experience in the implementation of a module for creating user profiles of Web visitors by using Zones, Weights and Actions. The module is part of Gugubarra 2.0, a tool for better understanding and management of communities of registered Web visitors, currently being developed by the database group at the Computer Science Institute of the Goethe University in Frankfurt. In addition we introduce a semantic concept for our approach. 1.

Abstract In order to be capable of exploiting context for pro-active information recommendation, agents need to extract and understand user activities based on their knowledge of the user interests. In this paper, we propose a novel approach for context-aware recommendation in browsing assistants based on the integration of user profiles, navigational patterns and contextual elements. In this approach, user profiles built using an unsupervised Web page clustering algorithm are used to characterize user ongoing activities and behavior patterns. Experimental evidence show that using longer-term interests to explain active browsing goals user assistance is effectively enhanced. Keywords: User Profiling, Context-awareness, Browsing Assistants.