ARTIFICIAL IMMUNE SYSTEMS FOR INFORMATION FILTERING: FOCUSING ON PROFILE ADAPTATION

The human immune system has characteristics such as self-organisation, robustness and adaptivity that may be useful in the development of adaptive systems. One suitable application area for adaptive systems is Information Filtering (IF). Within the context of IF, learning and adapting user profiles is an important research area. In an individual profile, an IF system has to rely on the ability of the user profile to maintain a satisfactory level of filtering accuracy for as long as it is being used. This thesis explores a possible way to enable Artificial Immune Systems (AIS) to filter information in the context of profile adaptation. Previous work has investigated this issue from the perspective of self-organisation based on Autopoetic Theory. In contrast, this current work approaches the problem from the perspective of diversity inspired by the concept of dynamic clonal selection and gene library to maintain sufficient diversity. An immune inspired IF for profile adaptation is proposed and developed. This algorithm is demonstrated to work in detecting relevant documents by using a single profile to recognize a user’s interests and to adapt to changes in them. We employed a virtual user tested on a web document corpus to test the profile on learning of an emerging new topic of interest and forgetting uninteresting topics. The results clearly indicate the profile’s ability to adapt to frequent variations and radical changes in user interest. This work has focused on textual information, but it may have the potential to be applied in other media such as audio and images in which adaptivity to dynamic environments is crucial. These are all interesting future directions in which this work might develop.