MASACAD: A multi-agent approach to information customization for the purpose of academic advising of students

The growth and advancement in the Internet and the World Wide Web has led to an explosion in the amount of available information. This staggering amount of information has made it extremely difficult for users to locate and retrieve information that is actually relevant to their task at hand. Dealing with this problem of “information overload” will need tools to customize the information space. In this paper we present MASACAD, a multi-agent system that learns to advise students by mining the Web and discuss important problems in relationship to information customization systems and smooth the way for possible solutions. The main idea is to approach information customization using a multi-agent paradigm in combination with a number of aspects from the domains of machine learning, user modeling, and Web mining.