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

Not published at Aalto University

Macquarie University

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › 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
Publisher	IEEE
Pages	63-70
Number of pages	8
ISBN (Electronic)	9781467372787
DOIs	https://doi.org/10.1109/BigDataCongress.2015.19
Publication status	Published - 17 Aug 2015
MoE publication type	A4 Conference publication
Event	IEEE International Conference on Big Data - New York City, United States Duration: 27 Jun 2015 → 2 Jul 2015 Conference number: 4

Conference

Conference	IEEE International Conference on Big Data
Abbreviated title	BigData
Country/Territory	United States
City	New York City
Period	27/06/2015 → 02/07/2015

Keywords

Recommender Systems
Robust Matrix Factorization
Social Media
Social Recommendation
Tie Strength

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10.1109/BigDataCongress.2015.19

Cite this

@inproceedings{6b4af7d8ea3940a788f85cc3a6290321,

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, Robust Matrix Factorization, Social Media, Social Recommendation, 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",

pages = "63--70",

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

publisher = "IEEE",

address = "United States",

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

}

TY - GEN

T1 - Incorporating Tie Strength in Robust Social Recommendation

AU - Zhong, Youliang

AU - Yang, Jian

AU - Nugroho, Robertus

N1 - Conference code: 4

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 - Robust Matrix Factorization

KW - Social Media

KW - Social Recommendation

KW - Tie Strength

UR - http://www.scopus.com/inward/record.url?scp=84959540254&partnerID=8YFLogxK

U2 - 10.1109/BigDataCongress.2015.19

DO - 10.1109/BigDataCongress.2015.19

M3 - Conference article in proceedings

AN - SCOPUS:84959540254

SP - 63

EP - 70

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

PB - IEEE

T2 - IEEE International Conference on Big Data

Y2 - 27 June 2015 through 2 July 2015

ER -

Incorporating Tie Strength in Robust Social Recommendation

Abstract

Conference

Keywords

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