On the Effects of Idiotypic Interactions for Recommendation Communities in Artificial Immune Systems

TL;DR

This paper investigates how idiotypic interactions in artificial immune system-based recommenders influence their performance, revealing that they produce distinct neighborhoods and utilize idiotypic effects to improve recommendation quality.

Contribution

It provides new insights into the role of idiotypic interactions in enhancing recommender systems through neighborhood differences and weighting strategies.

Findings

Immune system recommenders create different neighborhoods than correlation-based methods.

Superior performance partly due to neighborhood differences.

Use of idiotypic effects to weight neighbors' recommendations improves results.

Abstract

It has previously been shown that a recommender based on immune system idiotypic principles can out perform one based on correlation alone. This paper reports the results of work in progress, where we undertake some investigations into the nature of this beneficial effect. The initial findings are that the immune system recommender tends to produce different neighbourhoods, and that the superior performance of this recommender is due partly to the different neighbourhoods, and partly to the way that the idiotypic effect is used to weight each neighbours recommendations.