Incorporating tie strength in robust social recommendation

Youliang Zhong; Jian Yang; Robertus Nugroho

doi:10.1109/BigDataCongress.2015.19

Incorporating tie strength in robust social recommendation

Youliang Zhong, Jian Yang, Robertus Nugroho

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

5 Citations (Scopus)

Abstract

In this paper, we present a novel method in making recommendations by leveraging Tie Strength, an integrated social relationship measurement calculated from various user information gathered from social media. Moreover, the proposed method adopts Least Absolute Errors in factorization scheme to reduce the sensitivity to data outliers. We have conducted comprehensive experiments over the real datasets from popular social media services. The evaluation results demonstrate that the proposed method outperforms certain state-of-The-Art social recommendation methods in terms of Root Mean Squared Error and Precision versus Recall measures.

Original language	English
Title of host publication	Proceedings - 2015 IEEE International Congress on Big Data, BigData Congress 2015
Editors	Carminati Barbara, Latifur Khan
Place of Publication	Piscataway, NJ
Publisher	Institute of Electrical and Electronics Engineers (IEEE)
Pages	63-70
Number of pages	8
ISBN (Electronic)	9781467372787, 9781467372770
ISBN (Print)	9781467372794
DOIs	https://doi.org/10.1109/BigDataCongress.2015.19
Publication status	Published - 17 Aug 2015
Event	4th IEEE International Congress on Big Data, BigData Congress 2015 - New York City, United States Duration: 27 Jun 2015 → 2 Jul 2015

Other

Other	4th IEEE International Congress on Big Data, BigData Congress 2015
Country/Territory	United States
City	New York City
Period	27/06/15 → 2/07/15

Keywords

Recommender Systems
Social Media
Social Recommendation
Robust Matrix Factorization
Tie Strength

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10.1109/BigDataCongress.2015.19

Cite this

@inproceedings{013c1a2db89a4cc6b56423d4a0ad5cba,

title = "Incorporating tie strength in robust social recommendation",

abstract = "In this paper, we present a novel method in making recommendations by leveraging Tie Strength, an integrated social relationship measurement calculated from various user information gathered from social media. Moreover, the proposed method adopts Least Absolute Errors in factorization scheme to reduce the sensitivity to data outliers. We have conducted comprehensive experiments over the real datasets from popular social media services. The evaluation results demonstrate that the proposed method outperforms certain state-of-The-Art social recommendation methods in terms of Root Mean Squared Error and Precision versus Recall measures.",

keywords = "Recommender Systems, Social Media, Social Recommendation, Robust Matrix Factorization, Tie Strength",

author = "Youliang Zhong and Jian Yang and Robertus Nugroho",

year = "2015",

month = aug,

day = "17",

doi = "10.1109/BigDataCongress.2015.19",

language = "English",

isbn = "9781467372794",

pages = "63--70",

editor = "Carminati Barbara and Latifur Khan",

booktitle = "Proceedings - 2015 IEEE International Congress on Big Data, BigData Congress 2015",

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

address = "United States",

note = "4th IEEE International Congress on Big Data, BigData Congress 2015 ; Conference date: 27-06-2015 Through 02-07-2015",

}

Zhong, Y, Yang, J & Nugroho, R 2015, Incorporating tie strength in robust social recommendation. in C Barbara & L Khan (eds), Proceedings - 2015 IEEE International Congress on Big Data, BigData Congress 2015. Institute of Electrical and Electronics Engineers (IEEE), Piscataway, NJ, pp. 63-70, 4th IEEE International Congress on Big Data, BigData Congress 2015, New York City, United States, 27/06/15. https://doi.org/10.1109/BigDataCongress.2015.19

Incorporating tie strength in robust social recommendation. / Zhong, Youliang; Yang, Jian; Nugroho, Robertus.
Proceedings - 2015 IEEE International Congress on Big Data, BigData Congress 2015. ed. / Carminati Barbara; Latifur Khan. Piscataway, NJ: Institute of Electrical and Electronics Engineers (IEEE), 2015. p. 63-70.

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

TY - GEN

T1 - Incorporating tie strength in robust social recommendation

AU - Zhong, Youliang

AU - Yang, Jian

AU - Nugroho, Robertus

PY - 2015/8/17

Y1 - 2015/8/17

N2 - In this paper, we present a novel method in making recommendations by leveraging Tie Strength, an integrated social relationship measurement calculated from various user information gathered from social media. Moreover, the proposed method adopts Least Absolute Errors in factorization scheme to reduce the sensitivity to data outliers. We have conducted comprehensive experiments over the real datasets from popular social media services. The evaluation results demonstrate that the proposed method outperforms certain state-of-The-Art social recommendation methods in terms of Root Mean Squared Error and Precision versus Recall measures.

AB - In this paper, we present a novel method in making recommendations by leveraging Tie Strength, an integrated social relationship measurement calculated from various user information gathered from social media. Moreover, the proposed method adopts Least Absolute Errors in factorization scheme to reduce the sensitivity to data outliers. We have conducted comprehensive experiments over the real datasets from popular social media services. The evaluation results demonstrate that the proposed method outperforms certain state-of-The-Art social recommendation methods in terms of Root Mean Squared Error and Precision versus Recall measures.

KW - Recommender Systems

KW - Social Media

KW - Social Recommendation

KW - Robust Matrix Factorization

KW - Tie Strength

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U2 - 10.1109/BigDataCongress.2015.19

DO - 10.1109/BigDataCongress.2015.19

M3 - Conference proceeding contribution

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SN - 9781467372794

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EP - 70

BT - Proceedings - 2015 IEEE International Congress on Big Data, BigData Congress 2015

A2 - Barbara, Carminati

A2 - Khan, Latifur

PB - Institute of Electrical and Electronics Engineers (IEEE)

CY - Piscataway, NJ

T2 - 4th IEEE International Congress on Big Data, BigData Congress 2015

Y2 - 27 June 2015 through 2 July 2015

ER -

Incorporating tie strength in robust social recommendation

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