Back to Search
Start Over
Aligning Users Across Social Networks by Joint User and Label Consistence Representation
- Source :
- Neural Information Processing ISBN: 9783030367107, ICONIP (2)
- Publication Year :
- 2019
- Publisher :
- Springer International Publishing, 2019.
-
Abstract
- Aligning users belonging to the same person in different social networks has attracted much attention. Recently, embedding methods have been proposed to represent users from different social networks into vector spaces with same dimension. To handle the challenge of vector space diversity, existing methods usually make vectors of known aligned users closer/consistent and overlap different vector spaces. However, compared to large amount of users in each social network, the consistence constraint on aligned users is not enough to reduce the diversity. Besides, missing edges/labels may provide incorrect information and affect the effect of the overlap between learned vector spaces. Therefore, we propose the OURLACER method, i.e, jOint UseR and LAbel ConsistencE Representation, to jointly represent each user and label under the consistence constraints of know aligned users and shared labels. Specifically, OURLACER utilizes collective matrix factorization to complete missing edges and labels for each user, which can provide sufficient information to distinguish each user. Moreover, OURLACER adds the consistence constraint on shared labels in different social networks. Because each user has own labels, label consistence can restrict each user and greatly reduce the diversity between learned vector spaces. Extensive experiments conducted on real-world datasets demonstrate that our method significantly outperforms other state-of-the-art methods.
- Subjects :
- Information retrieval
Social network
business.industry
Computer science
02 engineering and technology
Matrix decomposition
Constraint (information theory)
020204 information systems
0202 electrical engineering, electronic engineering, information engineering
Embedding
020201 artificial intelligence & image processing
Dimension (data warehouse)
business
Representation (mathematics)
Subjects
Details
- ISBN :
- 978-3-030-36710-7
- ISBNs :
- 9783030367107
- Database :
- OpenAIRE
- Journal :
- Neural Information Processing ISBN: 9783030367107, ICONIP (2)
- Accession number :
- edsair.doi...........b9513f3e2f0965d585e7934b01133c87