About: User Identity Linkage (UIL) is the problem of matching user identities across multiple online social networks (OSNs) which belong to the same person. The solutions to UIL problem facilitate cross-platform research on OSN users and enable many useful applications such as user profiling and recommendation. As the UIL labeled data are often lacking and costly to obtain, learning user embeddings for matching user identities using an unsupervised approach is therefore highly desired. In this paper, we propose a novel unsupervised UIL framework for enhancing existing user embedding-based UIL methods. Our proposed framework incorporates two key ideas, user-discriminative features and retrofitting embedding. The user-discriminative features enable us to differentiate a specific user identity from other users in its OSN. From the user-discriminative features, we derive pairs of similar user identities across OSNs for retrofitting the base user embeddings of existing UIL methods. Through extensive experiments on three real-world OSN datasets, we show that our framework can leverage user-discriminative features to improve the accuracy of different user embedding-based UIL methods significantly. The quantum of improvement can also be surprisingly good even for existing UIL methods with very poor matching accuracy. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this chapter (10.1007/978-3-030-47426-3_30) contains supplementary material, which is available to authorized users.   Goto Sponge  NotDistinct  Permalink

An Entity of Type : fabio:Abstract, within Data Space : wasabi.inria.fr associated with source document(s)

AttributesValues
type
value
  • User Identity Linkage (UIL) is the problem of matching user identities across multiple online social networks (OSNs) which belong to the same person. The solutions to UIL problem facilitate cross-platform research on OSN users and enable many useful applications such as user profiling and recommendation. As the UIL labeled data are often lacking and costly to obtain, learning user embeddings for matching user identities using an unsupervised approach is therefore highly desired. In this paper, we propose a novel unsupervised UIL framework for enhancing existing user embedding-based UIL methods. Our proposed framework incorporates two key ideas, user-discriminative features and retrofitting embedding. The user-discriminative features enable us to differentiate a specific user identity from other users in its OSN. From the user-discriminative features, we derive pairs of similar user identities across OSNs for retrofitting the base user embeddings of existing UIL methods. Through extensive experiments on three real-world OSN datasets, we show that our framework can leverage user-discriminative features to improve the accuracy of different user embedding-based UIL methods significantly. The quantum of improvement can also be surprisingly good even for existing UIL methods with very poor matching accuracy. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this chapter (10.1007/978-3-030-47426-3_30) contains supplementary material, which is available to authorized users.
subject
  • Interoperability
  • Technology in society
  • Music conferences
  • Music education in the United States
  • 1910 establishments in Texas
  • Education in Texas
  • Music education organizations
  • Organizations based in Austin, Texas
  • Organizations based in Texas
  • Sports in Texas
  • Texas classical music
  • University Interscholastic League
part of
is abstract of
is hasSource of
Faceted Search & Find service v1.13.91 as of Mar 24 2020


Alternative Linked Data Documents: Sponger | ODE     Content Formats:       RDF       ODATA       Microdata      About   
This material is Open Knowledge   W3C Semantic Web Technology [RDF Data]
OpenLink Virtuoso version 07.20.3229 as of Jul 10 2020, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (94 GB total memory)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2025 OpenLink Software