Learning social relationship strength via matrix co-factorization with multiple kernels

Youliang Zhong, Lan Du, Jian Yang

Research output: Chapter in Book/Report/Conference proceedingConference proceeding contribution

2 Citations (Scopus)

Abstract

In recent years the research on measuring relationship strength among the people in a social network has gained attention due to its potential applications of social network analysis. The challenge is how we can learn social relationship strength based on various resources such as user profiles and social interactions. In this paper we propose a KPMCF model to learn social relationship strength based on users' latent features inferred from both profile and interaction information. The proposed model takes an uniformed approach of integrating Matrix Co-Factorization with Multiple Kernels. We conduct experiments on real-world data sets for typical web mining applications, showing that the proposed model produces better relationship strength measurement in comparison with other social factors.

Original languageEnglish
Title of host publicationWeb Information Systems Engineering, WISE 2013
Subtitle of host publication14th International Conference, Nanjing, China, October 13-15, 2013, Proceedings, Part 1
EditorsXuemin Lin, Yannis Manolopoulos, Divesh Srivastava, Guangyan Huang
Place of PublicationHeidelberg
PublisherSpringer, Springer Nature
Pages15-28
Number of pages14
ISBN (Electronic)9783642412301
ISBN (Print)9783642412295
DOIs
Publication statusPublished - 2013
Event14th International Conference on Web Information Systems Engineering, WISE 2013 - Nanjing, China
Duration: 13 Oct 201315 Oct 2013

Publication series

NameLecture Notes in Computer Science
PublisherSpringer Berlin Heidelberg
Volume8180
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other14th International Conference on Web Information Systems Engineering, WISE 2013
CountryChina
CityNanjing
Period13/10/1315/10/13

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