RLINK: Deep reinforcement learning for user identity linkage

Xiaoxue LI, Yanan CAO, Qian LI, Yanmin SHANG, Yangxi LI, Yanbing LIU, Guandong XU

Research output: Contribution to journalArticlespeer-review

20 Citations (Scopus)

Abstract

User identity linkage is a task of recognizing the identities of the same user across different social networks (SN). Previous works tackle this problem via estimating the pairwise similarity between identities from different SN, predicting the label of identity pairs or selecting the most relevant identity pair based on the similarity scores. However, most of these methods fail to utilize the results of previously matched identities, which could contribute to the subsequent linkages in following matching steps. To address this problem, we transform user identity linkage into a sequence decision problem and propose a reinforcement learning model to optimize the linkage strategy from the global perspective. Our method makes full use of both the social network structure and the history matched identities, meanwhile explores the long-term influence of processing matching on subsequent decisions. We conduct extensive experiments on real-world datasets, the results show that our method outperforms the state-of-the-art methods. Copyright © 2020 The Author(s).

Original languageEnglish
Pages (from-to)85-103
JournalWorld Wide Web
Volume24
Early online dateAug 2020
DOIs
Publication statusPublished - Jan 2021

Citation

Li, X., Cao, Y., Li, Q., Shang, Y., Li, Y., Liu, Y., & Xu, G. (2021). RLINK: Deep reinforcement learning for user identity linkage. World Wide Web, 24, 85-103. https://doi.org/10.1007/s11280-020-00833-8

Keywords

  • Social network
  • Reinforcement learning
  • User identity linkage
  • Markov decision process

Fingerprint

Dive into the research topics of 'RLINK: Deep reinforcement learning for user identity linkage'. Together they form a unique fingerprint.