Emergence of Spontaneous Order Through Neighborhood Formation in Peer-to-Peer Recommender Systems

TL;DR

This paper proposes a decentralized peer-to-peer recommender system architecture that self-organizes neighborhoods of similar users using epidemic protocols, demonstrating scalability and high-quality recommendations through experiments on MovieLens.

Contribution

It introduces a novel agent-based framework combining P2P computing with recommender systems, utilizing epidemic protocols for neighborhood formation.

Findings

The system maintains scalable and robust neighborhoods.

It converges to high-quality recommendations.

Experimental results validate effectiveness on MovieLens dataset.

Abstract

The advent of the Semantic Web necessitates paradigm shifts away from centralized client/server architectures towards decentralization and peer-to-peer computation, making the existence of central authorities superfluous and even impossible. At the same time, recommender systems are gaining considerable impact in e-commerce, providing people with recommendations that are personalized and tailored to their very needs. These recommender systems have traditionally been deployed with stark centralized scenarios in mind, operating in closed communities detached from their host network's outer perimeter. We aim at marrying these two worlds, i.e., decentralized peer-to-peer computing and recommender systems, in one agent-based framework. Our architecture features an epidemic-style protocol maintaining neighborhoods of like-minded peers in a robust, selforganizing fashion. In order to demonstrate our architecture's ability to retain scalability, robustness and to allow for convergence towards high-quality recommendations, we conduct offline experiments on top of the popular MovieLens dataset.