Jorge Morais

Abstract

Abstract
Due to the large amount of pages in Websites it is important to collect knowledge about users' previous visits in order to provide patterns that allow the customization of the Website. In previous work we proposed a multi-agent approach using agents with two different algorithms (associative rules and collaborative filtering) and showed the results of the offline tests. Both algorithms are incremental and work with binary data. In this paper we present the results of experiments held online. Results show that this multi-agent approach combining different algorithms is capable of improving user's satisfaction.

Abstract
The large amount of pages in Websites is a problem for users who waste time looking for the information they really want. Knowledge about users' previous visits may provide patterns that allow the customization of the Website. This concept is known as Adaptive Website: a Website that adapts itself for the purpose of improving the user's experience. Some Web Mining algorithms have been proposed for adapting a Website. In this paper, a recommender system using agents with two different algorithms (associative rules and collaborative filtering) is described. Both algorithms are incremental and work with binary data. Results show that this multi-agent approach combining different algorithms is capable of improving user's satisfaction.

Abstract
Web growth has brought several problems to users. The large amount of information that exists nowadays in some particular Websites turns the task of finding useful information very difficult. Knowing users' visiting pattern is crucial to owners, so that they may transform or customize the Website. This problem originated the concept known as Adaptive Website: a Website that adapts itself for the purpose of improving the user's experience. This paper describes a proposal for a doctoral thesis. The main goal of this work is to follow a multi-agent approach for Web adaptation. The idea is that all knowledge administration about the Website and its users, and the use of that knowledge to adapt the site to fulfil user's needs, are made by an autonomous intelligent agent society in a negotiation environment. The complexity of the problem and the inherently distributed nature of the Web, which is an open, heterogeneous and decentralized network, are reasons that justify the multi-agent approa