Work

Decentralized Adaptive Ranking using Transformers

Year: 2025

Type: conference-paper

Abstract: Centralized platforms like TikTok are cause for significant concerns over information control, censorship, and bias. Decentralized systems offer a promising alternative, but their adoption is hindered by the lack of effective relevance ranking of search results. Existing decentralized approaches rely on heuristics that do not adapt to user behavior. This paper presents DART, the first decentralized ranking algorithm to leverage machine learning over users' search activities. DART adapts its ranking function using a Transformer-based learning-to-rank model trained on a real workload from a decentralized file-sharing application. We find that it improves over the best baseline by 19 % on our ranking metric (MRR).

DOI: 10.1145/3721146.3721945

Authors: Marcel Gregoriadis, Quinten Stokkink, Johan Pouwelse

Institution: Delft University of Technology

Language: English

Topic: Information Retrieval and Search Behavior

Subfield: Information Systems

Field: Computer Science

Domain: Physical Sciences

Open Access status: gold

OpenAlex ID: W4409088575

FWCI: 1.215

Cites: 15

Cited by: 1

Related to: 10