Nowadays web portals contain large amount of information that is meant for various visitors or groups of visitors. To effectively navigate within the content the website needs to "know" its users in order to provide personalized content to them. We propose a method for automatic estimation of the user's interest in a web page he visits. This estimation is used for the recommendation of web portal pages (through presenting adaptive links) that the user might like. We conducted series of experiments in the domain of our faculty web portal to evaluate proposed approach.

Visualization of large urban complexes on the Web is a highly demanding task both from the networking and computational point of view. The whole three-dimensional model of a city is mostly too big to be downloaded in a reasonable time and to be stored in a memory of commonly used computers. This paper presents a scalable approach for subdividing an urban model into smaller parts. Dynamic loading and unloading data allows theoretically unlimited extension of a model with constant network and computation load. Further requirements specific to presentation of virtual cities are also discussed. Theoretical considerations are followed by a practical implementation utilizing the VRML language for modeling the historical city of Prague.