Using Trust of Social Ties for Recommendation

Liang CHEN; Chengcheng SHAO; Peidong ZHU; Haoyang ZHU

doi:10.1587/transinf.2015EDP7199

Using Trust of Social Ties for Recommendation

Liang CHEN, Chengcheng SHAO, Peidong ZHU, Haoyang ZHU

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Nowadays, with the development of online social networks (OSN), a mass of online social information has been generated in OSN, which has triggered research on social recommendation. Collaborative filtering, as one of the most popular techniques in social recommendation, faces several challenges, such as data sparsity, cold-start users and prediction quality. The motivation of our work is to deal with the above challenges by effectively combining collaborative filtering technology with social information. The trust relationship has been identified as a useful means of using social information to improve the quality of recommendation. In this paper, we propose a trust-based recommendation approach which uses GlobalTrust (GT) to represent the trust value among users as neighboring nodes. A matrix factorization based on singular value decomposition is used to get a trust network built on the GT value. The recommendation results are obtained through a modified random walk algorithm called GlobalTrustWalker. Through experiments on a real-world sparser dataset, we demonstrate that the proposed approach can better utilize users' social trust information and improve the recommendation accuracy on cold-start users.

Publication: IEICE TRANSACTIONS on Information Vol.E99-D No.2 pp.397-405

Publication Date: 2016/02/01

Publicized: 2015/10/30

Online ISSN: 1745-1361

DOI: 10.1587/transinf.2015EDP7199

Type of Manuscript: PAPER

Category: Data Engineering, Web Information Systems

Authors

Liang CHEN
  National University of Defense Technology
Chengcheng SHAO
  National University of Defense Technology
Peidong ZHU
  National University of Defense Technology
Haoyang ZHU
  National University of Defense Technology

Keyword

social network, trust-based, collaborative filtering, random walk

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Liang CHEN, Chengcheng SHAO, Peidong ZHU, Haoyang ZHU, "Using Trust of Social Ties for Recommendation" in IEICE TRANSACTIONS on Information, vol. E99-D, no. 2, pp. 397-405, February 2016, doi: 10.1587/transinf.2015EDP7199.
Abstract: Nowadays, with the development of online social networks (OSN), a mass of online social information has been generated in OSN, which has triggered research on social recommendation. Collaborative filtering, as one of the most popular techniques in social recommendation, faces several challenges, such as data sparsity, cold-start users and prediction quality. The motivation of our work is to deal with the above challenges by effectively combining collaborative filtering technology with social information. The trust relationship has been identified as a useful means of using social information to improve the quality of recommendation. In this paper, we propose a trust-based recommendation approach which uses GlobalTrust (GT) to represent the trust value among users as neighboring nodes. A matrix factorization based on singular value decomposition is used to get a trust network built on the GT value. The recommendation results are obtained through a modified random walk algorithm called GlobalTrustWalker. Through experiments on a real-world sparser dataset, we demonstrate that the proposed approach can better utilize users' social trust information and improve the recommendation accuracy on cold-start users.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.2015EDP7199/_p

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@ARTICLE{e99-d_2_397,
author={Liang CHEN, Chengcheng SHAO, Peidong ZHU, Haoyang ZHU, },
journal={IEICE TRANSACTIONS on Information},
title={Using Trust of Social Ties for Recommendation},
year={2016},
volume={E99-D},
number={2},
pages={397-405},
abstract={Nowadays, with the development of online social networks (OSN), a mass of online social information has been generated in OSN, which has triggered research on social recommendation. Collaborative filtering, as one of the most popular techniques in social recommendation, faces several challenges, such as data sparsity, cold-start users and prediction quality. The motivation of our work is to deal with the above challenges by effectively combining collaborative filtering technology with social information. The trust relationship has been identified as a useful means of using social information to improve the quality of recommendation. In this paper, we propose a trust-based recommendation approach which uses GlobalTrust (GT) to represent the trust value among users as neighboring nodes. A matrix factorization based on singular value decomposition is used to get a trust network built on the GT value. The recommendation results are obtained through a modified random walk algorithm called GlobalTrustWalker. Through experiments on a real-world sparser dataset, we demonstrate that the proposed approach can better utilize users' social trust information and improve the recommendation accuracy on cold-start users.},
keywords={},
doi={10.1587/transinf.2015EDP7199},
ISSN={1745-1361},
month={February},}

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TY - JOUR
TI - Using Trust of Social Ties for Recommendation
T2 - IEICE TRANSACTIONS on Information
SP - 397
EP - 405
AU - Liang CHEN
AU - Chengcheng SHAO
AU - Peidong ZHU
AU - Haoyang ZHU
PY - 2016
DO - 10.1587/transinf.2015EDP7199
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E99-D
IS - 2
JA - IEICE TRANSACTIONS on Information
Y1 - February 2016
AB - Nowadays, with the development of online social networks (OSN), a mass of online social information has been generated in OSN, which has triggered research on social recommendation. Collaborative filtering, as one of the most popular techniques in social recommendation, faces several challenges, such as data sparsity, cold-start users and prediction quality. The motivation of our work is to deal with the above challenges by effectively combining collaborative filtering technology with social information. The trust relationship has been identified as a useful means of using social information to improve the quality of recommendation. In this paper, we propose a trust-based recommendation approach which uses GlobalTrust (GT) to represent the trust value among users as neighboring nodes. A matrix factorization based on singular value decomposition is used to get a trust network built on the GT value. The recommendation results are obtained through a modified random walk algorithm called GlobalTrustWalker. Through experiments on a real-world sparser dataset, we demonstrate that the proposed approach can better utilize users' social trust information and improve the recommendation accuracy on cold-start users.
ER -