SSL-STR: semi-supervised learning for sparse trust recommendation

Zhengdi Hu; Guangquan Xu; Xi Zheng; Jiang Liu; Xiaojiang Du

doi:10.1109/GLOBECOM38437.2019.9014085

SSL-STR: semi-supervised learning for sparse trust recommendation

Zhengdi Hu, Guangquan Xu, Xi Zheng, Jiang Liu^*, Xiaojiang Du

^*Corresponding author for this work

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

2 Citations (Scopus)

Abstract

Trust is widely applied in recommender systems to improve recommendation performance by alleviating well-known problems, such as cold start, data sparsity, and so on. However, trust data itself also faces sparse problems. To solve these problems, we propose a novel sparse trust recommendation model, SSL-STR. Specifically, we decompose the aspects influencing trust-building into finer-grained factors, and combine these factors to mine the implicit sparse trust relationships among users by employing the Transductive Support Vector Machine algorithm. Then we extend SVD++ model with social trust and sparse trust information for rating prediction in the recommendation system. Experiments show that our SSL-STR improves the recommendation accuracy by up to 4.3%.

Original language	English
Title of host publication	2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings
Place of Publication	Piscataway, NJ
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	1-6
Number of pages	6
ISBN (Electronic)	9781728109626
DOIs	https://doi.org/10.1109/GLOBECOM38437.2019.9014085
Publication status	Published - 2019
Event	2019 IEEE Global Communications Conference - Waikoloa, United States Duration: 9 Dec 2019 → 13 Dec 2019

Conference

Conference	2019 IEEE Global Communications Conference
Abbreviated title	IEEE GLOBECOM 2019
Country/Territory	United States
City	Waikoloa
Period	9/12/19 → 13/12/19

Keywords

Recommendation system
Sparse trust
SSL-STR
SVD++
Transductive Support Vector Machine

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10.1109/GLOBECOM38437.2019.9014085

Cite this

@inproceedings{02ebe97880ae4faf8f242351b2d6228e,

title = "SSL-STR: semi-supervised learning for sparse trust recommendation",

abstract = "Trust is widely applied in recommender systems to improve recommendation performance by alleviating well-known problems, such as cold start, data sparsity, and so on. However, trust data itself also faces sparse problems. To solve these problems, we propose a novel sparse trust recommendation model, SSL-STR. Specifically, we decompose the aspects influencing trust-building into finer-grained factors, and combine these factors to mine the implicit sparse trust relationships among users by employing the Transductive Support Vector Machine algorithm. Then we extend SVD++ model with social trust and sparse trust information for rating prediction in the recommendation system. Experiments show that our SSL-STR improves the recommendation accuracy by up to 4.3\%.",

keywords = "Recommendation system, Sparse trust, SSL-STR, SVD++, Transductive Support Vector Machine",

author = "Zhengdi Hu and Guangquan Xu and Xi Zheng and Jiang Liu and Xiaojiang Du",

year = "2019",

doi = "10.1109/GLOBECOM38437.2019.9014085",

language = "English",

pages = "1--6",

booktitle = "2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings",

publisher = "Institute of Electrical and Electronics Engineers (IEEE)",

address = "United States",

note = "2019 IEEE Global Communications Conference, IEEE GLOBECOM 2019 ; Conference date: 09-12-2019 Through 13-12-2019",

}

Hu, Z, Xu, G, Zheng, X, Liu, J & Du, X 2019, SSL-STR: semi-supervised learning for sparse trust recommendation. in 2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings. Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, pp. 1-6, 2019 IEEE Global Communications Conference, Waikoloa, Hawaii, United States, 9/12/19. https://doi.org/10.1109/GLOBECOM38437.2019.9014085

SSL-STR: semi-supervised learning for sparse trust recommendation. / Hu, Zhengdi; Xu, Guangquan; Zheng, Xi et al.
2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2019. p. 1-6.

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

TY - GEN

T1 - SSL-STR

T2 - 2019 IEEE Global Communications Conference

AU - Hu, Zhengdi

AU - Xu, Guangquan

AU - Zheng, Xi

AU - Liu, Jiang

AU - Du, Xiaojiang

PY - 2019

Y1 - 2019

N2 - Trust is widely applied in recommender systems to improve recommendation performance by alleviating well-known problems, such as cold start, data sparsity, and so on. However, trust data itself also faces sparse problems. To solve these problems, we propose a novel sparse trust recommendation model, SSL-STR. Specifically, we decompose the aspects influencing trust-building into finer-grained factors, and combine these factors to mine the implicit sparse trust relationships among users by employing the Transductive Support Vector Machine algorithm. Then we extend SVD++ model with social trust and sparse trust information for rating prediction in the recommendation system. Experiments show that our SSL-STR improves the recommendation accuracy by up to 4.3%.

AB - Trust is widely applied in recommender systems to improve recommendation performance by alleviating well-known problems, such as cold start, data sparsity, and so on. However, trust data itself also faces sparse problems. To solve these problems, we propose a novel sparse trust recommendation model, SSL-STR. Specifically, we decompose the aspects influencing trust-building into finer-grained factors, and combine these factors to mine the implicit sparse trust relationships among users by employing the Transductive Support Vector Machine algorithm. Then we extend SVD++ model with social trust and sparse trust information for rating prediction in the recommendation system. Experiments show that our SSL-STR improves the recommendation accuracy by up to 4.3%.

KW - Recommendation system

KW - Sparse trust

KW - SSL-STR

KW - SVD++

KW - Transductive Support Vector Machine

UR - https://www.scopus.com/pages/publications/85081983272

U2 - 10.1109/GLOBECOM38437.2019.9014085

DO - 10.1109/GLOBECOM38437.2019.9014085

M3 - Conference proceeding contribution

SP - 1

EP - 6

BT - 2019 IEEE Global Communications Conference, GLOBECOM 2019 - Proceedings

PB - Institute of Electrical and Electronics Engineers (IEEE)

CY - Piscataway, NJ

Y2 - 9 December 2019 through 13 December 2019

ER -

SSL-STR: semi-supervised learning for sparse trust recommendation

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Keywords

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