LsRec: Large-scale social recommendation with online update

Research output: Contribution to journalJournal articlepeer-review

Standard

LsRec : Large-scale social recommendation with online update. / Zhou, Wang; Zhou, Yongluan; Li, Jianping; Memon, Muhammad Hammad.

In: Expert Systems with Applications, Vol. 162, 113739, 2020.

Research output: Contribution to journalJournal articlepeer-review

Harvard

Zhou, W, Zhou, Y, Li, J & Memon, MH 2020, 'LsRec: Large-scale social recommendation with online update', Expert Systems with Applications, vol. 162, 113739. https://doi.org/10.1016/j.eswa.2020.113739

APA

Zhou, W., Zhou, Y., Li, J., & Memon, M. H. (2020). LsRec: Large-scale social recommendation with online update. Expert Systems with Applications, 162, [113739]. https://doi.org/10.1016/j.eswa.2020.113739

Vancouver

Zhou W, Zhou Y, Li J, Memon MH. LsRec: Large-scale social recommendation with online update. Expert Systems with Applications. 2020;162. 113739. https://doi.org/10.1016/j.eswa.2020.113739

Author

Zhou, Wang ; Zhou, Yongluan ; Li, Jianping ; Memon, Muhammad Hammad. / LsRec : Large-scale social recommendation with online update. In: Expert Systems with Applications. 2020 ; Vol. 162.

Bibtex

@article{7901a0c5e5c94ff4b34ee6d74390851e,
title = "LsRec: Large-scale social recommendation with online update",
abstract = "With the ever-increasing scale and complexity of social network and online business, Recommender Systems (RS) have played crucial roles in information processing and filtering in various online applications, although suffering from such as data sparsity and low accuracy problems. Meanwhile, recent researches try to enhance the performance of RS through such social network and clustering algorithms, however, they may fail to achieve further improvement in large-scale online recommendation due to the serious information overload. In this article, a novel social recommendation approach with online update referred to as LsRec is proposed, which generally contains offline computation and online incremental update. More precisely, LsRec not only takes account of user's social relationship, but also clusters items according to the similarity degree, furthermore, LsRec performs recommendation in each generated cluster respectively. In practice, LsRec could be capable of exploiting user-level social influence, and capturing the intricate relationship between items. In addition, theoretical proof could provide convergence guarantee for the model. Specifically, with the appealing merit of flexible online update scenario, LsRec could yield high performance in large-scale online recommendation with low computational complexity. Extensive experimental analysis over four real world datasets demonstrate the effectiveness and efficiency of LsRec, which indicates that LsRec could significantly outperform state-of-the-art recommender approaches, especially in large-scale online recommendation.",
keywords = "Item clustering, Matrix factorization, Online update, Social recommendation",
author = "Wang Zhou and Yongluan Zhou and Jianping Li and Memon, {Muhammad Hammad}",
year = "2020",
doi = "10.1016/j.eswa.2020.113739",
language = "English",
volume = "162",
journal = "Expert Systems with Applications",
issn = "0957-4174",
publisher = "Pergamon Press",

}

RIS

TY - JOUR

T1 - LsRec

T2 - Large-scale social recommendation with online update

AU - Zhou, Wang

AU - Zhou, Yongluan

AU - Li, Jianping

AU - Memon, Muhammad Hammad

PY - 2020

Y1 - 2020

N2 - With the ever-increasing scale and complexity of social network and online business, Recommender Systems (RS) have played crucial roles in information processing and filtering in various online applications, although suffering from such as data sparsity and low accuracy problems. Meanwhile, recent researches try to enhance the performance of RS through such social network and clustering algorithms, however, they may fail to achieve further improvement in large-scale online recommendation due to the serious information overload. In this article, a novel social recommendation approach with online update referred to as LsRec is proposed, which generally contains offline computation and online incremental update. More precisely, LsRec not only takes account of user's social relationship, but also clusters items according to the similarity degree, furthermore, LsRec performs recommendation in each generated cluster respectively. In practice, LsRec could be capable of exploiting user-level social influence, and capturing the intricate relationship between items. In addition, theoretical proof could provide convergence guarantee for the model. Specifically, with the appealing merit of flexible online update scenario, LsRec could yield high performance in large-scale online recommendation with low computational complexity. Extensive experimental analysis over four real world datasets demonstrate the effectiveness and efficiency of LsRec, which indicates that LsRec could significantly outperform state-of-the-art recommender approaches, especially in large-scale online recommendation.

AB - With the ever-increasing scale and complexity of social network and online business, Recommender Systems (RS) have played crucial roles in information processing and filtering in various online applications, although suffering from such as data sparsity and low accuracy problems. Meanwhile, recent researches try to enhance the performance of RS through such social network and clustering algorithms, however, they may fail to achieve further improvement in large-scale online recommendation due to the serious information overload. In this article, a novel social recommendation approach with online update referred to as LsRec is proposed, which generally contains offline computation and online incremental update. More precisely, LsRec not only takes account of user's social relationship, but also clusters items according to the similarity degree, furthermore, LsRec performs recommendation in each generated cluster respectively. In practice, LsRec could be capable of exploiting user-level social influence, and capturing the intricate relationship between items. In addition, theoretical proof could provide convergence guarantee for the model. Specifically, with the appealing merit of flexible online update scenario, LsRec could yield high performance in large-scale online recommendation with low computational complexity. Extensive experimental analysis over four real world datasets demonstrate the effectiveness and efficiency of LsRec, which indicates that LsRec could significantly outperform state-of-the-art recommender approaches, especially in large-scale online recommendation.

KW - Item clustering

KW - Matrix factorization

KW - Online update

KW - Social recommendation

UR - http://www.scopus.com/inward/record.url?scp=85088821007&partnerID=8YFLogxK

U2 - 10.1016/j.eswa.2020.113739

DO - 10.1016/j.eswa.2020.113739

M3 - Journal article

AN - SCOPUS:85088821007

VL - 162

JO - Expert Systems with Applications

JF - Expert Systems with Applications

SN - 0957-4174

M1 - 113739

ER -

ID: 250213697