Exploiting users’ rating behaviour to enhance the robustness of social recommendation

Zizhu Zhang; Weiliang Zhao; Jian Yang; Surya Nepal; Cecile Paris; Bing Li

doi:10.1007/978-3-319-68786-5_37

Exploiting users’ rating behaviour to enhance the robustness of social recommendation

Zizhu Zhang^*, Weiliang Zhao, Jian Yang, Surya Nepal, Cecile Paris, Bing Li

^*Corresponding author for this work

School of Computing

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

2 Citations (Scopus)

Abstract

In the rating systems, quite often it can be observed that some users rate few items, whereas some users rate a large number of items. Users’ rating scores also vary, i.e., some users’ scores are widely distributed while others are falling in a small range. Existing social recommendation approaches largely ignore such differences. We propose a peer-based relay recommendation method that exploits the credibility of users’ ratings. The credibility of a user’s rating is calculated according to its rating behaviour in terms of the number of ratings provided and the deviation from the normal behaviour. The credibility value of a user’s rating is incorporated when aggregating ratings from different users. Experiments are conducted on a large-scale social rating network for movie recommendations. The results show that the incorporation of credibility of users’ ratings can effectively reduce the impact of recommended rating noises with low credibility and enhance robustness of the system.

Original language	English
Title of host publication	Web Information Systems Engineering – WISE 2017
Subtitle of host publication	18th International Conference, Puschino, Russia, October 7–11, 2017. Proceedings, Part II
Editors	Athman Bouguettaya, Yunjun Gao, Andrey Klimenko, Lu Chen, Xiangliang Zhang, Fedor Dzerzhinskiy, Weijia Jia, Stanislav V. Klimenko, Qing Li
Place of Publication	Cham
Publisher	Springer, Springer Nature
Pages	467-475
Number of pages	9
ISBN (Electronic)	9783319687865
ISBN (Print)	9783319687858
DOIs	https://doi.org/10.1007/978-3-319-68786-5_37
Publication status	Published - 2017
Event	18th International Conference on Web Information Systems Engineering, WISE 2017 - Puschino, Russian Federation Duration: 7 Oct 2017 → 11 Oct 2017

Publication series

Name	Lecture Notes in Computer Science
Volume	10570
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	18th International Conference on Web Information Systems Engineering, WISE 2017
Country/Territory	Russian Federation
City	Puschino
Period	7/10/17 → 11/10/17

Keywords

Credibility
Peer-based recommendation
Rating behaviour
Recommendation relay scheme
Social recommender system

Access to Document

10.1007/978-3-319-68786-5_37

Cite this

Zhang, Z., Zhao, W., Yang, J., Nepal, S., Paris, C., & Li, B. (2017). Exploiting users’ rating behaviour to enhance the robustness of social recommendation. In A. Bouguettaya, Y. Gao, A. Klimenko, L. Chen, X. Zhang, F. Dzerzhinskiy, W. Jia, S. V. Klimenko, & Q. Li (Eds.), Web Information Systems Engineering – WISE 2017: 18th International Conference, Puschino, Russia, October 7–11, 2017. Proceedings, Part II (pp. 467-475). (Lecture Notes in Computer Science; Vol. 10570). Springer, Springer Nature. https://doi.org/10.1007/978-3-319-68786-5_37

Zhang, Zizhu ; Zhao, Weiliang ; Yang, Jian et al. / Exploiting users’ rating behaviour to enhance the robustness of social recommendation. Web Information Systems Engineering – WISE 2017: 18th International Conference, Puschino, Russia, October 7–11, 2017. Proceedings, Part II. editor / Athman Bouguettaya ; Yunjun Gao ; Andrey Klimenko ; Lu Chen ; Xiangliang Zhang ; Fedor Dzerzhinskiy ; Weijia Jia ; Stanislav V. Klimenko ; Qing Li. Cham : Springer, Springer Nature, 2017. pp. 467-475 (Lecture Notes in Computer Science).

@inproceedings{4ee71d19f1fc4f94a880620cb5221a7f,

title = "Exploiting users{\textquoteright} rating behaviour to enhance the robustness of social recommendation",

abstract = "In the rating systems, quite often it can be observed that some users rate few items, whereas some users rate a large number of items. Users{\textquoteright} rating scores also vary, i.e., some users{\textquoteright} scores are widely distributed while others are falling in a small range. Existing social recommendation approaches largely ignore such differences. We propose a peer-based relay recommendation method that exploits the credibility of users{\textquoteright} ratings. The credibility of a user{\textquoteright}s rating is calculated according to its rating behaviour in terms of the number of ratings provided and the deviation from the normal behaviour. The credibility value of a user{\textquoteright}s rating is incorporated when aggregating ratings from different users. Experiments are conducted on a large-scale social rating network for movie recommendations. The results show that the incorporation of credibility of users{\textquoteright} ratings can effectively reduce the impact of recommended rating noises with low credibility and enhance robustness of the system.",

keywords = "Credibility, Peer-based recommendation, Rating behaviour, Recommendation relay scheme, Social recommender system",

author = "Zizhu Zhang and Weiliang Zhao and Jian Yang and Surya Nepal and Cecile Paris and Bing Li",

year = "2017",

doi = "10.1007/978-3-319-68786-5_37",

language = "English",

isbn = "9783319687858",

series = "Lecture Notes in Computer Science",

publisher = "Springer, Springer Nature",

pages = "467--475",

editor = "Athman Bouguettaya and Yunjun Gao and Andrey Klimenko and Lu Chen and Xiangliang Zhang and Fedor Dzerzhinskiy and Weijia Jia and Klimenko, {Stanislav V.} and Qing Li",

booktitle = "Web Information Systems Engineering – WISE 2017",

address = "United States",

note = "18th International Conference on Web Information Systems Engineering, WISE 2017 ; Conference date: 07-10-2017 Through 11-10-2017",

}

Zhang, Z, Zhao, W , Yang, J, Nepal, S, Paris, C & Li, B 2017, Exploiting users’ rating behaviour to enhance the robustness of social recommendation. in A Bouguettaya, Y Gao, A Klimenko, L Chen, X Zhang, F Dzerzhinskiy, W Jia, SV Klimenko & Q Li (eds), Web Information Systems Engineering – WISE 2017: 18th International Conference, Puschino, Russia, October 7–11, 2017. Proceedings, Part II. Lecture Notes in Computer Science, vol. 10570, Springer, Springer Nature, Cham, pp. 467-475, 18th International Conference on Web Information Systems Engineering, WISE 2017, Puschino, Russian Federation, 7/10/17. https://doi.org/10.1007/978-3-319-68786-5_37

Exploiting users’ rating behaviour to enhance the robustness of social recommendation. / Zhang, Zizhu; Zhao, Weiliang ; Yang, Jian et al.
Web Information Systems Engineering – WISE 2017: 18th International Conference, Puschino, Russia, October 7–11, 2017. Proceedings, Part II. ed. / Athman Bouguettaya; Yunjun Gao; Andrey Klimenko; Lu Chen; Xiangliang Zhang; Fedor Dzerzhinskiy; Weijia Jia; Stanislav V. Klimenko; Qing Li. Cham: Springer, Springer Nature, 2017. p. 467-475 (Lecture Notes in Computer Science; Vol. 10570).

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

TY - GEN

T1 - Exploiting users’ rating behaviour to enhance the robustness of social recommendation

AU - Zhang, Zizhu

AU - Zhao, Weiliang

AU - Yang, Jian

AU - Nepal, Surya

AU - Paris, Cecile

AU - Li, Bing

PY - 2017

Y1 - 2017

N2 - In the rating systems, quite often it can be observed that some users rate few items, whereas some users rate a large number of items. Users’ rating scores also vary, i.e., some users’ scores are widely distributed while others are falling in a small range. Existing social recommendation approaches largely ignore such differences. We propose a peer-based relay recommendation method that exploits the credibility of users’ ratings. The credibility of a user’s rating is calculated according to its rating behaviour in terms of the number of ratings provided and the deviation from the normal behaviour. The credibility value of a user’s rating is incorporated when aggregating ratings from different users. Experiments are conducted on a large-scale social rating network for movie recommendations. The results show that the incorporation of credibility of users’ ratings can effectively reduce the impact of recommended rating noises with low credibility and enhance robustness of the system.

AB - In the rating systems, quite often it can be observed that some users rate few items, whereas some users rate a large number of items. Users’ rating scores also vary, i.e., some users’ scores are widely distributed while others are falling in a small range. Existing social recommendation approaches largely ignore such differences. We propose a peer-based relay recommendation method that exploits the credibility of users’ ratings. The credibility of a user’s rating is calculated according to its rating behaviour in terms of the number of ratings provided and the deviation from the normal behaviour. The credibility value of a user’s rating is incorporated when aggregating ratings from different users. Experiments are conducted on a large-scale social rating network for movie recommendations. The results show that the incorporation of credibility of users’ ratings can effectively reduce the impact of recommended rating noises with low credibility and enhance robustness of the system.

KW - Credibility

KW - Peer-based recommendation

KW - Rating behaviour

KW - Recommendation relay scheme

KW - Social recommender system

UR - http://www.scopus.com/inward/record.url?scp=85031406658&partnerID=8YFLogxK

U2 - 10.1007/978-3-319-68786-5_37

DO - 10.1007/978-3-319-68786-5_37

M3 - Conference proceeding contribution

AN - SCOPUS:85031406658

SN - 9783319687858

T3 - Lecture Notes in Computer Science

SP - 467

EP - 475

BT - Web Information Systems Engineering – WISE 2017

A2 - Bouguettaya, Athman

A2 - Gao, Yunjun

A2 - Klimenko, Andrey

A2 - Chen, Lu

A2 - Zhang, Xiangliang

A2 - Dzerzhinskiy, Fedor

A2 - Jia, Weijia

A2 - Klimenko, Stanislav V.

A2 - Li, Qing

PB - Springer, Springer Nature

CY - Cham

T2 - 18th International Conference on Web Information Systems Engineering, WISE 2017

Y2 - 7 October 2017 through 11 October 2017

ER -

Zhang Z, Zhao W , Yang J, Nepal S, Paris C, Li B. Exploiting users’ rating behaviour to enhance the robustness of social recommendation. In Bouguettaya A, Gao Y, Klimenko A, Chen L, Zhang X, Dzerzhinskiy F, Jia W, Klimenko SV, Li Q, editors, Web Information Systems Engineering – WISE 2017: 18th International Conference, Puschino, Russia, October 7–11, 2017. Proceedings, Part II. Cham: Springer, Springer Nature. 2017. p. 467-475. (Lecture Notes in Computer Science). doi: 10.1007/978-3-319-68786-5_37

Exploiting users’ rating behaviour to enhance the robustness of social recommendation

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this