  Artificial Immune Systems have been used successfully to build recommender
systems for film databases. In this research, an attempt is made to extend this
idea to web site recommendation. A collection of more than 1000 individuals web
profiles (alternatively called preferences / favourites / bookmarks file) will
be used. URLs will be classified using the DMOZ (Directory Mozilla) database of
the Open Directory Project as our ontology. This will then be used as the data
for the Artificial Immune Systems rather than the actual addresses. The first
attempt will involve using a simple classification code number coupled with the
number of pages within that classification code. However, this implementation
does not make use of the hierarchical tree-like structure of DMOZ.
Consideration will then be given to the construction of a similarity measure
for web profiles that makes use of this hierarchical information to build a
better-informed Artificial Immune System.
