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

Youliang Zhong; Lan Du; Jian Yang

doi:10.1007/978-3-642-41230-1_2

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

Youliang Zhong, Lan Du, Jian Yang

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

5 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 language	English
Title of host publication	Web Information Systems Engineering, WISE 2013
Subtitle of host publication	14th International Conference, Nanjing, China, October 13-15, 2013, Proceedings, Part 1
Editors	Xuemin Lin, Yannis Manolopoulos, Divesh Srivastava, Guangyan Huang
Place of Publication	Heidelberg
Publisher	Springer, Springer Nature
Pages	15-28
Number of pages	14
ISBN (Electronic)	9783642412301
ISBN (Print)	9783642412295
DOIs	https://doi.org/10.1007/978-3-642-41230-1_2
Publication status	Published - 2013
Event	14th International Conference on Web Information Systems Engineering, WISE 2013 - Nanjing, China Duration: 13 Oct 2013 → 15 Oct 2013

Publication series

Name	Lecture Notes in Computer Science
Publisher	Springer Berlin Heidelberg
Volume	8180
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	14th International Conference on Web Information Systems Engineering, WISE 2013
Country/Territory	China
City	Nanjing
Period	13/10/13 → 15/10/13

Access to Document

10.1007/978-3-642-41230-1_2

Cite this

Zhong, Y., Du, L., & Yang, J. (2013). Learning social relationship strength via matrix co-factorization with multiple kernels. In X. Lin, Y. Manolopoulos, D. Srivastava, & G. Huang (Eds.), Web Information Systems Engineering, WISE 2013: 14th International Conference, Nanjing, China, October 13-15, 2013, Proceedings, Part 1 (pp. 15-28). (Lecture Notes in Computer Science; Vol. 8180). Springer, Springer Nature. https://doi.org/10.1007/978-3-642-41230-1_2

Zhong, Youliang ; Du, Lan ; Yang, Jian. / Learning social relationship strength via matrix co-factorization with multiple kernels. Web Information Systems Engineering, WISE 2013: 14th International Conference, Nanjing, China, October 13-15, 2013, Proceedings, Part 1. editor / Xuemin Lin ; Yannis Manolopoulos ; Divesh Srivastava ; Guangyan Huang. Heidelberg : Springer, Springer Nature, 2013. pp. 15-28 (Lecture Notes in Computer Science).

@inproceedings{87141ddced6a47fb8ddeec971a3316c0,

title = "Learning social relationship strength via matrix co-factorization with multiple kernels",

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.",

author = "Youliang Zhong and Lan Du and Jian Yang",

year = "2013",

doi = "10.1007/978-3-642-41230-1_2",

language = "English",

isbn = "9783642412295",

series = "Lecture Notes in Computer Science",

publisher = "Springer, Springer Nature",

pages = "15--28",

editor = "Xuemin Lin and Yannis Manolopoulos and Divesh Srivastava and Guangyan Huang",

booktitle = "Web Information Systems Engineering, WISE 2013",

address = "United States",

note = "14th International Conference on Web Information Systems Engineering, WISE 2013 ; Conference date: 13-10-2013 Through 15-10-2013",

}

Zhong, Y, Du, L & Yang, J 2013, Learning social relationship strength via matrix co-factorization with multiple kernels. in X Lin, Y Manolopoulos, D Srivastava & G Huang (eds), Web Information Systems Engineering, WISE 2013: 14th International Conference, Nanjing, China, October 13-15, 2013, Proceedings, Part 1. Lecture Notes in Computer Science, vol. 8180, Springer, Springer Nature, Heidelberg, pp. 15-28, 14th International Conference on Web Information Systems Engineering, WISE 2013, Nanjing, China, 13/10/13. https://doi.org/10.1007/978-3-642-41230-1_2

Learning social relationship strength via matrix co-factorization with multiple kernels. / Zhong, Youliang; Du, Lan; Yang, Jian.
Web Information Systems Engineering, WISE 2013: 14th International Conference, Nanjing, China, October 13-15, 2013, Proceedings, Part 1. ed. / Xuemin Lin; Yannis Manolopoulos; Divesh Srivastava; Guangyan Huang. Heidelberg: Springer, Springer Nature, 2013. p. 15-28 (Lecture Notes in Computer Science; Vol. 8180).

Research output: Chapter in Book/Report/Conference proceeding › Conference proceeding contribution › peer-review

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T1 - Learning social relationship strength via matrix co-factorization with multiple kernels

AU - Zhong, Youliang

AU - Du, Lan

AU - Yang, Jian

PY - 2013

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N2 - 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.

AB - 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.

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U2 - 10.1007/978-3-642-41230-1_2

DO - 10.1007/978-3-642-41230-1_2

M3 - Conference proceeding contribution

AN - SCOPUS:84887436093

SN - 9783642412295

T3 - Lecture Notes in Computer Science

SP - 15

EP - 28

BT - Web Information Systems Engineering, WISE 2013

A2 - Lin, Xuemin

A2 - Manolopoulos, Yannis

A2 - Srivastava, Divesh

A2 - Huang, Guangyan

PB - Springer, Springer Nature

CY - Heidelberg

T2 - 14th International Conference on Web Information Systems Engineering, WISE 2013

Y2 - 13 October 2013 through 15 October 2013

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Zhong Y, Du L, Yang J. Learning social relationship strength via matrix co-factorization with multiple kernels. In Lin X, Manolopoulos Y, Srivastava D, Huang G, editors, Web Information Systems Engineering, WISE 2013: 14th International Conference, Nanjing, China, October 13-15, 2013, Proceedings, Part 1. Heidelberg: Springer, Springer Nature. 2013. p. 15-28. (Lecture Notes in Computer Science). doi: 10.1007/978-3-642-41230-1_2

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

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