A Recommender System based on the Immune Network

TL;DR

This paper introduces an artificial immune system-based recommender that leverages immune system principles like diversity and matching to improve film recommendations, showing promising results compared to traditional collaborative filtering methods.

Contribution

It proposes a novel immune system-inspired collaborative filtering approach that emphasizes diversity and matching, differing from classical optimization-focused methods.

Findings

Supports the hypothesis that AIS can effectively identify good recommendation subsets

Demonstrates competitive performance against other collaborative filtering techniques

Highlights the importance of diversity in recommendation quality

Abstract

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 antibody - antibody interaction for diversity. Computational results are presented in support of this conjecture and compared to those found by other CF techniques.