G-Rank: Unsupervised Continuous Learn-to-Rank for Edge Devices in a P2P Network

TL;DR

G-Rank is an innovative unsupervised ranking algorithm tailored for decentralized peer-to-peer networks, enabling accurate and relevant search results on lightweight devices without centralized data or extensive training.

Contribution

The paper introduces G-Rank, a novel unsupervised, lightweight ranking method specifically designed for decentralized networks, eliminating the need for centralized data and training.

Findings

Achieves accurate rankings without centralized data

Operates efficiently on consumer devices

Maintains relevance even when disconnected from the network

Abstract

Ranking algorithms in traditional search engines are powered by enormous training data sets that are meticulously engineered and curated by a centralized entity. Decentralized peer-to-peer (p2p) networks such as torrenting applications and Web3 protocols deliberately eschew centralized databases and computational architectures when designing services and features. As such, robust search-and-rank algorithms designed for such domains must be engineered specifically for decentralized networks, and must be lightweight enough to operate on consumer-grade personal devices such as a smartphone or laptop computer. We introduce G-Rank, an unsupervised ranking algorithm designed exclusively for decentralized networks. We demonstrate that accurate, relevant ranking results can be achieved in fully decentralized networks without any centralized data aggregation, feature engineering, or model training. Furthermore, we show that such results are obtainable with minimal data preprocessing and computational overhead, and can still return highly relevant results even when a user's device is disconnected from the network. G-Rank is highly modular in design, is not limited to categorical data, and can be implemented in a variety of domains with minimal modification. The results herein show that unsupervised ranking models designed for decentralized p2p networks are not only viable, but worthy of further research.