research-article

FRec: a novel framework of recommending users and communities in social media

Authors:

Saurabh Kataria,

Tao LiAuthors Info & Claims

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

Pages 1765 - 1770

https://doi.org/10.1145/2505515.2505645

Published: 27 October 2013 Publication History

Abstract

In this paper, we propose a framework of recommending users and communities in social media. Given a user's profile, our framework is capable of recommending influential users and topic-cohesive interactive communities that are most relevant to the given user. In our framework, we present a generative topic model to discover user-oriented and community-oriented topics simultaneously, which enables us to capture the exact topic interests of users, as well as the focuses of communities. Extensive evaluation on a data set obtained from Twitter has demonstrated the effectiveness of our proposed framework compared with other probabilistic topic model based recommendation methods.

References

[1]

M. Al Hasan, V. Chaoji, S. Salem, and M. Zaki. Link prediction using supervised learning. In SDM'06: Workshop on Link Analysis, Counter-terrorism and Security, 2006.

[2]

D. Blei, A. Ng, and M. Jordan. Latent dirichlet allocation. the Journal of machine Learning research, 3:993--1022, 2003.

Digital Library

[3]

W. Chen, J. Chu, J. Luan, H. Bai, Y. Wang, and E. Chang. Collaborative filtering for orkut communities: discovery of user latent behavior. In Proc. of WWW, pages 681--690. ACM, 2009.

Digital Library

[4]

W. Chen, D. Zhang, and E. Chang. Combinational collaborative filtering for personalized community recommendation. In Proc. of SIGKDD, pages 115--123. ACM, 2008.

Digital Library

[5]

S. Fortunato. Community detection in graphs. Physics Reports, 486(3):75--174, 2010.

[6]

T. Griffiths and M. Steyvers. Finding scientific topics. Proceedings of the National Academy of Sciences of the United States of America, 101(Suppl 1):5228--5235, 2004.

[7]

P. Lawrence, B. Sergey, R. Motwani, and T. Winograd. The pagerank citation ranking: Bringing order to the web. Technical report, Stanford University, 1998.

[8]

D. Liben-Nowell and J. Kleinberg. The link-prediction problem for social networks. Journal of the American society for information science and technology, 58(7):1019--1031, 2007.

Digital Library

[9]

M. Pennacchiotti and S. Gurumurthy. Investigating topic models for social media user recommendation. In Proc. of WWW, pages 101--102. ACM, 2011.

Digital Library

[10]

A. Popescul and L. Ungar. Statistical relational learning for link prediction. In IJCAI workshop on learning statistical models from relational data, volume 2003, 2003.

[11]

M. Porter, J. Onnela, and P. Mucha. Communities in networks. Notices of the AMS, 56(9):1082--1097, 2009.

[12]

M. Rosen-Zvi, C. Chemudugunta, T. Griffiths, P. Smyth, and M. Steyvers. Learning author-topic models from text corpora. ACM Transactions on Information Systems, 28(1):4, 2010.

Digital Library

[13]

M. Rosen-Zvi, T. Griffiths, M. Steyvers, and P. Smyth. The author-topic model for authors and documents. In Proc. of UAI, pages 487--494, 2004.

Digital Library

[14]

M. Sachan, D. Contractor, T. Faruquie, and L. Subramaniam. Using content and interactions for discovering communities in social networks. In Proc. of WWW, pages 331--340. ACM, 2012.

Digital Library

[15]

Z. Xu, Y. Zhang, Y. Wu, and Q. Yang. Modeling user posting behavior on social media. In Proc. of SIGIR, pages 545--554. ACM, 2012.

Digital Library

[16]

T. Yang, R. Jin, Y. Chi, and S. Zhu. Combining link and content for community detection: a discriminative approach. In Proc. of SIGKDD, pages 927--936. ACM, 2009.

Digital Library

[17]

K. Yu, S. Yu, and V. Tresp. Soft clustering on graphs. Advances in Neural Information Processing Systems, 18:1553--1560, 2006.

[18]

D. Zhou, E. Manavoglu, J. Li, C. Giles, and H. Zha. Probabilistic models for discovering e-communities. In Proc. of WWW, pages 173--182, 2006.

Digital Library

Cited By

Ma RZhang QWang JCui LHuang XCollins-Thompson KMei QDavison BLiu YYilmaz E(2018)Mention Recommendation for Multimodal Microblog with Cross-attention Memory NetworkThe 41st International ACM SIGIR Conference on Research & Development in Information Retrieval10.1145/3209978.3210026(195-204)Online publication date: 27-Jun-2018
https://dl.acm.org/doi/10.1145/3209978.3210026
Xu KCai YMin HChen J(2018)Top-N Trustee Recommendation with Binary User Trust FeedbackDatabase Systems for Advanced Applications10.1007/978-3-319-91455-8_23(269-279)Online publication date: 12-May-2018
https://doi.org/10.1007/978-3-319-91455-8_23
Xu KCai YMin HZheng XXie HWong TSheth ANgonga AWang yChang EŚlęzak DFranczyk BAlt RTao X(2017)UIS-LDAProceedings of the International Conference on Web Intelligence10.1145/3106426.3106494(260-265)Online publication date: 23-Aug-2017
https://dl.acm.org/doi/10.1145/3106426.3106494
Show More Cited By

Index Terms

FRec: a novel framework of recommending users and communities in social media
1. Information systems
  1. Information retrieval
    1. Retrieval tasks and goals
      1. Document filtering
      2. Information extraction

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Published In

cover image ACM Conferences

CIKM '13: Proceedings of the 22nd ACM international conference on Information & Knowledge Management

October 2013

2612 pages

ISBN:9781450322638

DOI:10.1145/2505515

General Chairs:
Qi He
LinkedIn, USA
,
Arun Iyengar
IBM T.J. Watson Research Center, USA
,
Program Chairs:
Wolfgang Nejdl
L3S Research Center, Germany
,
Jian Pei
Simon Fraser University, Canada
,
Rajeev Rastogi
Amazon, India

Copyright © 2013 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 27 October 2013

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CIKM'13: 22nd ACM International Conference on Information and Knowledge Management

October 27 - November 1, 2013

California, San Francisco, USA

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CIKM '13 Paper Acceptance Rate 143 of 848 submissions, 17%;

Overall Acceptance Rate 1,861 of 8,427 submissions, 22%

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The 34th ACM International Conference on Information and Knowledge Management

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Cited By

Ma RZhang QWang JCui LHuang XCollins-Thompson KMei QDavison BLiu YYilmaz E(2018)Mention Recommendation for Multimodal Microblog with Cross-attention Memory NetworkThe 41st International ACM SIGIR Conference on Research & Development in Information Retrieval10.1145/3209978.3210026(195-204)Online publication date: 27-Jun-2018
https://dl.acm.org/doi/10.1145/3209978.3210026
Xu KCai YMin HChen J(2018)Top-N Trustee Recommendation with Binary User Trust FeedbackDatabase Systems for Advanced Applications10.1007/978-3-319-91455-8_23(269-279)Online publication date: 12-May-2018
https://doi.org/10.1007/978-3-319-91455-8_23
Xu KCai YMin HZheng XXie HWong TSheth ANgonga AWang yChang EŚlęzak DFranczyk BAlt RTao X(2017)UIS-LDAProceedings of the International Conference on Web Intelligence10.1145/3106426.3106494(260-265)Online publication date: 23-Aug-2017
https://dl.acm.org/doi/10.1145/3106426.3106494
Li TXie NZeng CZhou WZheng LJiang YYang YHa HXue WHuang YChen SNavlakha JIyengar S(2017)Data-Driven Techniques in Disaster Information ManagementACM Computing Surveys10.1145/301767850:1(1-45)Online publication date: 10-Mar-2017
https://dl.acm.org/doi/10.1145/3017678
Li LPeng WKataria SSun TLi T(2015)Recommending Users and Communities in Social MediaACM Transactions on Knowledge Discovery from Data10.1145/275728210:2(1-27)Online publication date: 26-Oct-2015
https://dl.acm.org/doi/10.1145/2757282

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