  The immune system is a complex biological system with a highly distributed,
adaptive and self-organising nature. This paper presents an Artificial Immune
System (AIS) that exploits some of these characteristics and is applied to the
task of film recommendation by Collaborative Filtering (CF). Natural evolution
and in particular the immune system have not been designed for classical
optimisation. However, for this problem, we are not interested in finding a
single optimum. Rather we intend to identify a sub-set of good matches on which
recommendations can be based. It is our hypothesis that an AIS built on two
central aspects of the biological immune system will be an ideal candidate to
achieve this: Antigen-antibody interaction for matching and idiotypic
antibody-antibody interaction for diversity. Computational results are
presented in support of this conjecture and compared to those found by other CF
techniques.
