An Artificial Immune System as a Recommender System for Web Sites

TL;DR

This paper explores using Artificial Immune Systems to recommend websites by classifying user profiles with DMOZ ontology and developing similarity measures that leverage hierarchical classification for improved recommendations.

Contribution

It extends AI immune system-based recommendation methods from films to websites, incorporating hierarchical classification for better accuracy.

Findings

Initial simple classification approach tested

Hierarchical similarity measures proposed

Potential for improved website recommendations

Abstract

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.