AIRS: Ant-Inspired Recommendation System

The goal of recommendation systems is to produce a set of meaningful suggestions for a group of users that can be useful for them. This paper introduces a multi-agent algorithm that builds a distributed recommendation system by exploiting nature-inspired techniques. The recommendable resources are recognized through a metadata represented of a bit string obtained by the application of a locality preserving hash function that maps similar resources into similar strings. Each agent works independently to replicate and wisely relocate the metadata. The agent operations are led by the application of ad-hoc probability functions. The outcome of this collective work will be a sorted logical overlay network that allows a fast recommendation service. Experimental analysis shows how the logical reorganization of metadata achieved by the agents can improve the performances of the recommendation system.