Print Email Facebook Twitter G-Rank: Unsupervised Continuous Learn-to-Rank for Edge Devices in a P2P Network Title G-Rank: Unsupervised Continuous Learn-to-Rank for Edge Devices in a P2P Network Author Gold, Andrew (TU Delft Electrical Engineering, Mathematics and Computer Science) Contributor Pouwelse, J.A. (mentor) Degree granting institution Delft University of Technology Programme Computer Science Date 2023-01-31 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. Subject Learn-to-Rankunsupervised learningP2P Networks To reference this document use: http://resolver.tudelft.nl/uuid:9e86595d-9be1-401c-8a38-e1f7189e23fc Bibliographical note https://github.com/awrgold/G-Rank Part of collection Student theses Document type master thesis Rights © 2023 Andrew Gold Files PDF Andrew_Gold_LTR_Thesis_Final_.pdf 1.4 MB Close viewer /islandora/object/uuid:9e86595d-9be1-401c-8a38-e1f7189e23fc/datastream/OBJ/view