Decentralized Adaptive Ranking using Transformers
Marcel Gregoriadis (TU Delft - Data-Intensive Systems)
Q.A. Stokkink (TU Delft - Data-Intensive Systems)
JA Pouwelse (TU Delft - Data-Intensive Systems)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
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).
help_outline