A Topic Community-based Method for Friend Recommendation in Online Social Networks via Joint Nonnegative Matrix Factorization

C. He; H. Li; Xiang Fei; Y. Tang; J. Zhu

doi:10.1109/CBD.2015.15

A Topic Community-based Method for Friend Recommendation in Online Social Networks via Joint Nonnegative Matrix Factorization

C. He, H. Li, Xiang Fei, Y. Tang, J. Zhu

Research output: Contribution to conference › Paper

11 Citations (Scopus)

Abstract

Online social networks (OSN) have become more and more popular and have accumulated a great many users. Friend recommendation can help users discover their interested friends and alleviate the problem of information overload. However, most of existing recommendation methods only consider user link or content information and hence are not effective enough to provide high quality recommendations. In this paper, we propose a topic community-based method via nonnegative matrix factorization (NMF). This method first applies joint NMF model to mine topic community existing in OSN by combing link and content information. Then it makes friend recommendation based on topic community. Experiments show that our method can reflect user preferences on friend selection more appropriately and has better recommendation performance than traditional methods. Moreover, our application case also demonstrates that it can obviously improve friend recommendation service in the real world OSN.

Original language	English
Pages	28 - 35
DOIs	https://doi.org/10.1109/CBD.2015.15
Publication status	Published - 2015
Event	International Conference on Advanced Cloud and Big Data - YangZhou, China Duration: 30 Oct 2015 → 1 Nov 2015

Conference

Conference	International Conference on Advanced Cloud and Big Data
Abbreviated title	CBD 2015
Country/Territory	China
City	YangZhou
Period	30/10/15 → 1/11/15

Bibliographical note

The full text is currently unavailable on the repository
© 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

Keywords

online social networks
topic community mining
friend recommendation
nonnegative matrix factorization
multiplicative update

Access to Document

10.1109/CBD.2015.15

Xiang Fei
- CEES School of Science - Assistant Professor Academic
Person: Teaching and Research

Cite this

@conference{74edc50d700443d69a9c138ca3f2d578,

title = "A Topic Community-based Method for Friend Recommendation in Online Social Networks via Joint Nonnegative Matrix Factorization",

abstract = "Online social networks (OSN) have become more and more popular and have accumulated a great many users. Friend recommendation can help users discover their interested friends and alleviate the problem of information overload. However, most of existing recommendation methods only consider user link or content information and hence are not effective enough to provide high quality recommendations. In this paper, we propose a topic community-based method via nonnegative matrix factorization (NMF). This method first applies joint NMF model to mine topic community existing in OSN by combing link and content information. Then it makes friend recommendation based on topic community. Experiments show that our method can reflect user preferences on friend selection more appropriately and has better recommendation performance than traditional methods. Moreover, our application case also demonstrates that it can obviously improve friend recommendation service in the real world OSN. ",

keywords = "online social networks, topic community mining, friend recommendation, nonnegative matrix factorization, multiplicative update",

author = "C. He and H. Li and Xiang Fei and Y. Tang and J. Zhu",

note = "The full text is currently unavailable on the repository {\textcopyright} 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.; International Conference on Advanced Cloud and Big Data, CBD 2015 ; Conference date: 30-10-2015 Through 01-11-2015",

year = "2015",

doi = "10.1109/CBD.2015.15",

language = "English",

pages = "28 -- 35",

}

TY - CONF

T1 - A Topic Community-based Method for Friend Recommendation in Online Social Networks via Joint Nonnegative Matrix Factorization

AU - He, C.

AU - Li, H.

AU - Fei, Xiang

AU - Tang, Y.

AU - Zhu, J.

N1 - The full text is currently unavailable on the repository © 2015 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.

PY - 2015

Y1 - 2015

N2 - Online social networks (OSN) have become more and more popular and have accumulated a great many users. Friend recommendation can help users discover their interested friends and alleviate the problem of information overload. However, most of existing recommendation methods only consider user link or content information and hence are not effective enough to provide high quality recommendations. In this paper, we propose a topic community-based method via nonnegative matrix factorization (NMF). This method first applies joint NMF model to mine topic community existing in OSN by combing link and content information. Then it makes friend recommendation based on topic community. Experiments show that our method can reflect user preferences on friend selection more appropriately and has better recommendation performance than traditional methods. Moreover, our application case also demonstrates that it can obviously improve friend recommendation service in the real world OSN.

AB - Online social networks (OSN) have become more and more popular and have accumulated a great many users. Friend recommendation can help users discover their interested friends and alleviate the problem of information overload. However, most of existing recommendation methods only consider user link or content information and hence are not effective enough to provide high quality recommendations. In this paper, we propose a topic community-based method via nonnegative matrix factorization (NMF). This method first applies joint NMF model to mine topic community existing in OSN by combing link and content information. Then it makes friend recommendation based on topic community. Experiments show that our method can reflect user preferences on friend selection more appropriately and has better recommendation performance than traditional methods. Moreover, our application case also demonstrates that it can obviously improve friend recommendation service in the real world OSN.

KW - online social networks

KW - topic community mining

KW - friend recommendation

KW - nonnegative matrix factorization

KW - multiplicative update

U2 - 10.1109/CBD.2015.15

DO - 10.1109/CBD.2015.15

M3 - Paper

SP - 28

EP - 35

T2 - International Conference on Advanced Cloud and Big Data

Y2 - 30 October 2015 through 1 November 2015

ER -

A Topic Community-based Method for Friend Recommendation in Online Social Networks via Joint Nonnegative Matrix Factorization

Abstract

Conference

Bibliographical note

Keywords

Access to Document

Fingerprint

Profiles

Xiang Fei

Cite this