Ranking-based multi-source rating aggregation with social contexts

Lei Li; Qin Liang; Guanfeng Liu

doi:10.1109/ICDMW.2015.20

Ranking-based multi-source rating aggregation with social contexts

Lei Li, Qin Liang, Guanfeng Liu

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

Abstract

Rating aggregation is critical to the quality control of recommendation systems and its effectiveness is a deep concern of all users. However, there are some problems in existing recommendation systems. For example, some of the raters from certain source are much more stringent than others, leading the phenomena that some entities with better quality are rejected. In this paper, we propose a novel raNking-based multI-source ratiNg Aggregation (NINA) approach. In this approach, based on the collected social context of recommenders and recommendees, the credibility of rankings of ratings from multiple sources can be estimated, and it can be used to deal with the disagreement during ranking-based rating aggregation from multiple sources. Hence, the proposed approach can effectively estimate the 'true' rating during aggregation based on the ratings from multiple sources, even though no prior knowledge exists about the distribution of stringent raters and lenient raters in different sources. We have studied the properties of NINA empirically. In particular, the experiments illustrate that compared with existing approaches, our proposed NINA can significantly reduce the influence of ratings from stringent raters and lenient raters, leading to trust enhanced rating aggregation, no matter what kind of the distributions of stringent raters and lenient raters are.

Original language	English
Title of host publication	Proceedings - 15th IEEE International Conference on Data Mining Workshop
Subtitle of host publication	ICDMW 2015
Editors	Peng Cui, Jennifer Dy, Charu Aggarwal, Zhi-Hua Zhou, Alexander Tuzhilin, Hui Xiong, Xindong Wu
Place of Publication	Los Alamitos
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	892-899
Number of pages	8
ISBN (Electronic)	9781467384926, 9781467384933
DOIs	https://doi.org/10.1109/ICDMW.2015.20
Publication status	Published - 29 Jan 2016
Externally published	Yes
Event	15th IEEE International Conference on Data Mining Workshop, ICDMW 2015 - Atlantic City, United States Duration: 14 Nov 2015 → 17 Nov 2015

Conference

Conference	15th IEEE International Conference on Data Mining Workshop, ICDMW 2015
Country/Territory	United States
City	Atlantic City
Period	14/11/15 → 17/11/15

Keywords

Multi-source
Ranking
Rating aggregation

Access to Document

10.1109/ICDMW.2015.20

Cite this

Li, L., Liang, Q., & Liu, G. (2016). Ranking-based multi-source rating aggregation with social contexts. In P. Cui, J. Dy, C. Aggarwal, Z-H. Zhou, A. Tuzhilin, H. Xiong, & X. Wu (Eds.), Proceedings - 15th IEEE International Conference on Data Mining Workshop: ICDMW 2015 (pp. 892-899). [7395762] Institute of Electrical and Electronics Engineers (IEEE). https://doi.org/10.1109/ICDMW.2015.20

Li, Lei ; Liang, Qin ; Liu, Guanfeng. / Ranking-based multi-source rating aggregation with social contexts. Proceedings - 15th IEEE International Conference on Data Mining Workshop: ICDMW 2015. editor / Peng Cui ; Jennifer Dy ; Charu Aggarwal ; Zhi-Hua Zhou ; Alexander Tuzhilin ; Hui Xiong ; Xindong Wu. Los Alamitos : Institute of Electrical and Electronics Engineers (IEEE), 2016. pp. 892-899

@inproceedings{d1484228695b4baaa0c9d86b073249a8,

title = "Ranking-based multi-source rating aggregation with social contexts",

abstract = "Rating aggregation is critical to the quality control of recommendation systems and its effectiveness is a deep concern of all users. However, there are some problems in existing recommendation systems. For example, some of the raters from certain source are much more stringent than others, leading the phenomena that some entities with better quality are rejected. In this paper, we propose a novel raNking-based multI-source ratiNg Aggregation (NINA) approach. In this approach, based on the collected social context of recommenders and recommendees, the credibility of rankings of ratings from multiple sources can be estimated, and it can be used to deal with the disagreement during ranking-based rating aggregation from multiple sources. Hence, the proposed approach can effectively estimate the 'true' rating during aggregation based on the ratings from multiple sources, even though no prior knowledge exists about the distribution of stringent raters and lenient raters in different sources. We have studied the properties of NINA empirically. In particular, the experiments illustrate that compared with existing approaches, our proposed NINA can significantly reduce the influence of ratings from stringent raters and lenient raters, leading to trust enhanced rating aggregation, no matter what kind of the distributions of stringent raters and lenient raters are.",

keywords = "Multi-source, Ranking, Rating aggregation",

author = "Lei Li and Qin Liang and Guanfeng Liu",

year = "2016",

month = jan,

day = "29",

doi = "10.1109/ICDMW.2015.20",

language = "English",

pages = "892--899",

editor = "Peng Cui and Jennifer Dy and Charu Aggarwal and Zhi-Hua Zhou and Alexander Tuzhilin and Hui Xiong and Xindong Wu",

booktitle = "Proceedings - 15th IEEE International Conference on Data Mining Workshop",

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

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note = "15th IEEE International Conference on Data Mining Workshop, ICDMW 2015 ; Conference date: 14-11-2015 Through 17-11-2015",

}

Li, L, Liang, Q & Liu, G 2016, Ranking-based multi-source rating aggregation with social contexts. in P Cui, J Dy, C Aggarwal, Z-H Zhou, A Tuzhilin, H Xiong & X Wu (eds), Proceedings - 15th IEEE International Conference on Data Mining Workshop: ICDMW 2015., 7395762, Institute of Electrical and Electronics Engineers (IEEE), Los Alamitos, pp. 892-899, 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015, Atlantic City, United States, 14/11/15. https://doi.org/10.1109/ICDMW.2015.20

Ranking-based multi-source rating aggregation with social contexts. / Li, Lei; Liang, Qin; Liu, Guanfeng.

Proceedings - 15th IEEE International Conference on Data Mining Workshop: ICDMW 2015. ed. / Peng Cui; Jennifer Dy; Charu Aggarwal; Zhi-Hua Zhou; Alexander Tuzhilin; Hui Xiong; Xindong Wu. Los Alamitos : Institute of Electrical and Electronics Engineers (IEEE), 2016. p. 892-899 7395762.

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

TY - GEN

T1 - Ranking-based multi-source rating aggregation with social contexts

AU - Li, Lei

AU - Liang, Qin

AU - Liu, Guanfeng

PY - 2016/1/29

Y1 - 2016/1/29

N2 - Rating aggregation is critical to the quality control of recommendation systems and its effectiveness is a deep concern of all users. However, there are some problems in existing recommendation systems. For example, some of the raters from certain source are much more stringent than others, leading the phenomena that some entities with better quality are rejected. In this paper, we propose a novel raNking-based multI-source ratiNg Aggregation (NINA) approach. In this approach, based on the collected social context of recommenders and recommendees, the credibility of rankings of ratings from multiple sources can be estimated, and it can be used to deal with the disagreement during ranking-based rating aggregation from multiple sources. Hence, the proposed approach can effectively estimate the 'true' rating during aggregation based on the ratings from multiple sources, even though no prior knowledge exists about the distribution of stringent raters and lenient raters in different sources. We have studied the properties of NINA empirically. In particular, the experiments illustrate that compared with existing approaches, our proposed NINA can significantly reduce the influence of ratings from stringent raters and lenient raters, leading to trust enhanced rating aggregation, no matter what kind of the distributions of stringent raters and lenient raters are.

AB - Rating aggregation is critical to the quality control of recommendation systems and its effectiveness is a deep concern of all users. However, there are some problems in existing recommendation systems. For example, some of the raters from certain source are much more stringent than others, leading the phenomena that some entities with better quality are rejected. In this paper, we propose a novel raNking-based multI-source ratiNg Aggregation (NINA) approach. In this approach, based on the collected social context of recommenders and recommendees, the credibility of rankings of ratings from multiple sources can be estimated, and it can be used to deal with the disagreement during ranking-based rating aggregation from multiple sources. Hence, the proposed approach can effectively estimate the 'true' rating during aggregation based on the ratings from multiple sources, even though no prior knowledge exists about the distribution of stringent raters and lenient raters in different sources. We have studied the properties of NINA empirically. In particular, the experiments illustrate that compared with existing approaches, our proposed NINA can significantly reduce the influence of ratings from stringent raters and lenient raters, leading to trust enhanced rating aggregation, no matter what kind of the distributions of stringent raters and lenient raters are.

KW - Multi-source

KW - Ranking

KW - Rating aggregation

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M3 - Conference proceeding contribution

AN - SCOPUS:84964720748

SP - 892

EP - 899

BT - Proceedings - 15th IEEE International Conference on Data Mining Workshop

A2 - Cui, Peng

A2 - Dy, Jennifer

A2 - Aggarwal, Charu

A2 - Zhou, Zhi-Hua

A2 - Tuzhilin, Alexander

A2 - Xiong, Hui

A2 - Wu, Xindong

PB - Institute of Electrical and Electronics Engineers (IEEE)

CY - Los Alamitos

T2 - 15th IEEE International Conference on Data Mining Workshop, ICDMW 2015

Y2 - 14 November 2015 through 17 November 2015

ER -

Li L, Liang Q, Liu G. Ranking-based multi-source rating aggregation with social contexts. In Cui P, Dy J, Aggarwal C, Zhou Z-H, Tuzhilin A, Xiong H, Wu X, editors, Proceedings - 15th IEEE International Conference on Data Mining Workshop: ICDMW 2015. Los Alamitos: Institute of Electrical and Electronics Engineers (IEEE). 2016. p. 892-899. 7395762 https://doi.org/10.1109/ICDMW.2015.20

Ranking-based multi-source rating aggregation with social contexts

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