Community-aware Social Recommendation: A Unified SCSVD Framework (Extended Abstract)

Jiewen Guan; Xin Huang; Bilian Chen

doi:10.1109/ICDE53745.2022.00133

Community-aware Social Recommendation: A Unified SCSVD Framework (Extended Abstract)

Jiewen Guan, Xin Huang, Bilian Chen^*

^*Corresponding author for this work

Department of Computer Science

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

Abstract

Social recommendation aims at improving recommendation performance by incorporating social information. Most existing social recommender systems only utilize the one-hop interpersonal social information, neglecting the community structure emerged in social networks, which may contain additional conducive information. In this paper, we propose a unified Simultaneous Community detection and Singular Value Decomposition (SCSVD) framework for community-aware social recommendation. An efficient optimization algorithm is also derived to optimize SCSVD, with an analysis of convergence and computational complexity. Comprehensive experimental results on three real-world benchmark datasets demonstrate the effectiveness of SCSVD, over both traditional matrix factorization based recommendation models and advanced neural network based recommendation models.

Original language	English
Title of host publication	2022 IEEE 38th International Conference on Data Engineering (ICDE)
Publisher	IEEE
Pages	1513-1514
Number of pages	2
ISBN (Electronic)	9781665408837
ISBN (Print)	9781665408844
DOIs	https://doi.org/10.1109/ICDE53745.2022.00133
Publication status	Published - 9 May 2022
Event	38th IEEE International Conference on Data Engineering, ICDE 2022 - Virtual, Kuala Lumpur, Malaysia Duration: 9 May 2022 → 12 May 2022 https://icde2022.ieeecomputer.my/ https://ieeexplore.ieee.org/xpl/conhome/9835153/proceeding

Publication series

Name	International Conference on Data Engineering
ISSN (Print)	1063-6382
ISSN (Electronic)	2375-026X

Conference

Conference	38th IEEE International Conference on Data Engineering, ICDE 2022
Country/Territory	Malaysia
City	Kuala Lumpur
Period	9/05/22 → 12/05/22
Internet address	https://icde2022.ieeecomputer.my/ https://ieeexplore.ieee.org/xpl/conhome/9835153/proceeding

Access to Document

10.1109/ICDE53745.2022.00133

Cite this

@inproceedings{881d5c1dff724d52a0181c5dcbe608da,

title = "Community-aware Social Recommendation: A Unified SCSVD Framework (Extended Abstract)",

abstract = "Social recommendation aims at improving recommendation performance by incorporating social information. Most existing social recommender systems only utilize the one-hop interpersonal social information, neglecting the community structure emerged in social networks, which may contain additional conducive information. In this paper, we propose a unified Simultaneous Community detection and Singular Value Decomposition (SCSVD) framework for community-aware social recommendation. An efficient optimization algorithm is also derived to optimize SCSVD, with an analysis of convergence and computational complexity. Comprehensive experimental results on three real-world benchmark datasets demonstrate the effectiveness of SCSVD, over both traditional matrix factorization based recommendation models and advanced neural network based recommendation models.",

author = "Jiewen Guan and Xin Huang and Bilian Chen",

note = "The work was supported in part by Youth Innovation Fund of Xiamen (Grant No. 3502Z20206049), National Natural Science Foundation of China (Grant No. 61836005) and HK RGC (Grant No. 22200320).; 38th IEEE International Conference on Data Engineering, ICDE 2022 ; Conference date: 09-05-2022 Through 12-05-2022",

year = "2022",

month = may,

day = "9",

doi = "10.1109/ICDE53745.2022.00133",

language = "English",

isbn = "9781665408844",

series = "International Conference on Data Engineering",

publisher = "IEEE",

pages = "1513--1514",

booktitle = "2022 IEEE 38th International Conference on Data Engineering (ICDE)",

address = "United States",

url = "https://icde2022.ieeecomputer.my/, https://ieeexplore.ieee.org/xpl/conhome/9835153/proceeding",

}

Guan, J, Huang, X & Chen, B 2022, Community-aware Social Recommendation: A Unified SCSVD Framework (Extended Abstract). in 2022 IEEE 38th International Conference on Data Engineering (ICDE). International Conference on Data Engineering, IEEE, pp. 1513-1514, 38th IEEE International Conference on Data Engineering, ICDE 2022, Kuala Lumpur, Malaysia, 9/05/22. https://doi.org/10.1109/ICDE53745.2022.00133

Community-aware Social Recommendation: A Unified SCSVD Framework (Extended Abstract). / Guan, Jiewen; Huang, Xin; Chen, Bilian.
2022 IEEE 38th International Conference on Data Engineering (ICDE). IEEE, 2022. p. 1513-1514 (International Conference on Data Engineering).

Research output: Chapter in book/report/conference proceeding › Conference proceeding › peer-review

TY - GEN

T1 - Community-aware Social Recommendation: A Unified SCSVD Framework (Extended Abstract)

AU - Guan, Jiewen

AU - Huang, Xin

AU - Chen, Bilian

N1 - The work was supported in part by Youth Innovation Fund of Xiamen (Grant No. 3502Z20206049), National Natural Science Foundation of China (Grant No. 61836005) and HK RGC (Grant No. 22200320).

PY - 2022/5/9

Y1 - 2022/5/9

N2 - Social recommendation aims at improving recommendation performance by incorporating social information. Most existing social recommender systems only utilize the one-hop interpersonal social information, neglecting the community structure emerged in social networks, which may contain additional conducive information. In this paper, we propose a unified Simultaneous Community detection and Singular Value Decomposition (SCSVD) framework for community-aware social recommendation. An efficient optimization algorithm is also derived to optimize SCSVD, with an analysis of convergence and computational complexity. Comprehensive experimental results on three real-world benchmark datasets demonstrate the effectiveness of SCSVD, over both traditional matrix factorization based recommendation models and advanced neural network based recommendation models.

AB - Social recommendation aims at improving recommendation performance by incorporating social information. Most existing social recommender systems only utilize the one-hop interpersonal social information, neglecting the community structure emerged in social networks, which may contain additional conducive information. In this paper, we propose a unified Simultaneous Community detection and Singular Value Decomposition (SCSVD) framework for community-aware social recommendation. An efficient optimization algorithm is also derived to optimize SCSVD, with an analysis of convergence and computational complexity. Comprehensive experimental results on three real-world benchmark datasets demonstrate the effectiveness of SCSVD, over both traditional matrix factorization based recommendation models and advanced neural network based recommendation models.

U2 - 10.1109/ICDE53745.2022.00133

DO - 10.1109/ICDE53745.2022.00133

M3 - Conference proceeding

SN - 9781665408844

T3 - International Conference on Data Engineering

SP - 1513

EP - 1514

BT - 2022 IEEE 38th International Conference on Data Engineering (ICDE)

PB - IEEE

T2 - 38th IEEE International Conference on Data Engineering, ICDE 2022

Y2 - 9 May 2022 through 12 May 2022

ER -

Community-aware Social Recommendation: A Unified SCSVD Framework (Extended Abstract)

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