Global and local influence-based social recommendation

Qinzhe Zhang; Jia Wu; Hong Yang; Weixue Lu; Guodong Long; Chengqi Zhang

doi:10.1145/2983323.2983873

Global and local influence-based social recommendation

Qinzhe Zhang, Jia Wu^*, Hong Yang, Weixue Lu, Guodong Long, Chengqi Zhang

^*Corresponding author for this work

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

12 Citations (Scopus)

Abstract

Social recommendation has been widely studied in recent years. Existing social recommendation models use various explicit pieces of social information as regularization terms, e.g., social links are considered as new constraints. However, social influence, an implicit source of information in social networks, is seldomly considered, even though it often drives recommendations in social networks. In this paper, we introduce a new global and local influence-based social recommendation model. Based on the observation that user purchase behaviour is influenced by both global influential nodes and the local influential nodes of the user, we formulate the global and local influence as an regularization terms, and incorporate them into a matrix factorization-based recommendation model. Experimental results on large data sets demonstrate the performance of the proposed method.

Original language	English
Title of host publication	CIKM 2016
Subtitle of host publication	Proceedings of the 25th ACM International on Conference on Information and Knowledge Management
Place of Publication	New York, NY
Publisher	Association for Computing Machinery
Pages	1917-1920
Number of pages	4
ISBN (Electronic)	9781450340731
DOIs	https://doi.org/10.1145/2983323.2983873
Publication status	Published - 24 Oct 2016
Externally published	Yes
Event	25th ACM International Conference on Information and Knowledge Management, CIKM 2016 - Indianapolis, United States Duration: 24 Oct 2016 → 28 Oct 2016

Conference

Conference	25th ACM International Conference on Information and Knowledge Management, CIKM 2016
Country	United States
City	Indianapolis
Period	24/10/16 → 28/10/16

Keywords

Dual social influence
Social recommendation

Access to Document

10.1145/2983323.2983873

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Cite this

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title = "Global and local influence-based social recommendation",

abstract = "Social recommendation has been widely studied in recent years. Existing social recommendation models use various explicit pieces of social information as regularization terms, e.g., social links are considered as new constraints. However, social influence, an implicit source of information in social networks, is seldomly considered, even though it often drives recommendations in social networks. In this paper, we introduce a new global and local influence-based social recommendation model. Based on the observation that user purchase behaviour is influenced by both global influential nodes and the local influential nodes of the user, we formulate the global and local influence as an regularization terms, and incorporate them into a matrix factorization-based recommendation model. Experimental results on large data sets demonstrate the performance of the proposed method.",

keywords = "Dual social influence, Social recommendation",

author = "Qinzhe Zhang and Jia Wu and Hong Yang and Weixue Lu and Guodong Long and Chengqi Zhang",

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doi = "10.1145/2983323.2983873",

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Zhang, Q, Wu, J, Yang, H, Lu, W, Long, G & Zhang, C 2016, Global and local influence-based social recommendation. in CIKM 2016: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. Association for Computing Machinery, New York, NY, pp. 1917-1920, 25th ACM International Conference on Information and Knowledge Management, CIKM 2016, Indianapolis, United States, 24/10/16. https://doi.org/10.1145/2983323.2983873

Global and local influence-based social recommendation. / Zhang, Qinzhe; Wu, Jia; Yang, Hong; Lu, Weixue; Long, Guodong; Zhang, Chengqi.

CIKM 2016: Proceedings of the 25th ACM International on Conference on Information and Knowledge Management. New York, NY : Association for Computing Machinery, 2016. p. 1917-1920.

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

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T1 - Global and local influence-based social recommendation

AU - Zhang, Qinzhe

AU - Wu, Jia

AU - Yang, Hong

AU - Lu, Weixue

AU - Long, Guodong

AU - Zhang, Chengqi

PY - 2016/10/24

Y1 - 2016/10/24

N2 - Social recommendation has been widely studied in recent years. Existing social recommendation models use various explicit pieces of social information as regularization terms, e.g., social links are considered as new constraints. However, social influence, an implicit source of information in social networks, is seldomly considered, even though it often drives recommendations in social networks. In this paper, we introduce a new global and local influence-based social recommendation model. Based on the observation that user purchase behaviour is influenced by both global influential nodes and the local influential nodes of the user, we formulate the global and local influence as an regularization terms, and incorporate them into a matrix factorization-based recommendation model. Experimental results on large data sets demonstrate the performance of the proposed method.

AB - Social recommendation has been widely studied in recent years. Existing social recommendation models use various explicit pieces of social information as regularization terms, e.g., social links are considered as new constraints. However, social influence, an implicit source of information in social networks, is seldomly considered, even though it often drives recommendations in social networks. In this paper, we introduce a new global and local influence-based social recommendation model. Based on the observation that user purchase behaviour is influenced by both global influential nodes and the local influential nodes of the user, we formulate the global and local influence as an regularization terms, and incorporate them into a matrix factorization-based recommendation model. Experimental results on large data sets demonstrate the performance of the proposed method.

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