Targeted dot product representation for friend recommendation in online social networks

Minh D. Dao; Akshay Rangamani; Sang Chin; Nam P. Nguyen; Trac D. Tran

doi:10.1145/2808797.2809414

Targeted dot product representation for friend recommendation in online social networks

Minh D. Dao, Akshay Rangamani, Sang Chin, Nam P. Nguyen, Trac D. Tran

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

Abstract

In this paper, we develop Targeted Dot Product Representation (TarDPR), a DPR-based feature selection and combination framework for friend recommendation in online social networks (OSNs). Our approach modifies conventional DPR techniques and makes itself applicable to OSNs by focusing on computing a consistent representation while minimizing unnecessary suggestions made outside these interested regions. A notable property of TarDPR is its ability to effectively incorporate different types of social features and produce new meaningful features that help competitive approaches to significantly improve their recommendation quality. We derive an iterative algorithm for TarDPR that is supported by mathematical analysis, and is efficient on large social traces. To certify the usability of our approach, we conduct empirical experiments on real social traces including Facebook and Foursquare social networks. The competitive experimental results show that TarDPR achieves up to 15% improvement in comparison with other competitive methods. These results consequently confirm the efficacy of our suggested framework.

Original language	English (US)
Title of host publication	Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
Editors	Jian Pei, Jie Tang, Fabrizio Silvestri
Publisher	Association for Computing Machinery, Inc
Pages	349-356
Number of pages	8
ISBN (Electronic)	9781450338547
DOIs	https://doi.org/10.1145/2808797.2809414
State	Published - Aug 25 2015
Externally published	Yes
Event	IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015 - Paris, France Duration: Aug 25 2015 → Aug 28 2015

Publication series

Name	Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015

Other

Other	IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015
Country/Territory	France
City	Paris
Period	8/25/15 → 8/28/15

All Science Journal Classification (ASJC) codes

Computer Science Applications
Computer Networks and Communications

Access to Document

10.1145/2808797.2809414

Cite this

Dao, M. D., Rangamani, A., Chin, S., Nguyen, N. P., & Tran, T. D. (2015). Targeted dot product representation for friend recommendation in online social networks. In J. Pei, J. Tang, & F. Silvestri (Eds.), Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015 (pp. 349-356). (Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015). Association for Computing Machinery, Inc. https://doi.org/10.1145/2808797.2809414

Dao, Minh D. ; Rangamani, Akshay ; Chin, Sang et al. / Targeted dot product representation for friend recommendation in online social networks. Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015. editor / Jian Pei ; Jie Tang ; Fabrizio Silvestri. Association for Computing Machinery, Inc, 2015. pp. 349-356 (Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015).

@inproceedings{c67317665c254a698e2c5e955c8fb293,

title = "Targeted dot product representation for friend recommendation in online social networks",

abstract = "In this paper, we develop Targeted Dot Product Representation (TarDPR), a DPR-based feature selection and combination framework for friend recommendation in online social networks (OSNs). Our approach modifies conventional DPR techniques and makes itself applicable to OSNs by focusing on computing a consistent representation while minimizing unnecessary suggestions made outside these interested regions. A notable property of TarDPR is its ability to effectively incorporate different types of social features and produce new meaningful features that help competitive approaches to significantly improve their recommendation quality. We derive an iterative algorithm for TarDPR that is supported by mathematical analysis, and is efficient on large social traces. To certify the usability of our approach, we conduct empirical experiments on real social traces including Facebook and Foursquare social networks. The competitive experimental results show that TarDPR achieves up to 15% improvement in comparison with other competitive methods. These results consequently confirm the efficacy of our suggested framework.",

author = "Dao, {Minh D.} and Akshay Rangamani and Sang Chin and Nguyen, {Nam P.} and Tran, {Trac D.}",

note = "Publisher Copyright: {\textcopyright} 2015 ACM.; IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015 ; Conference date: 25-08-2015 Through 28-08-2015",

year = "2015",

month = aug,

day = "25",

doi = "10.1145/2808797.2809414",

language = "English (US)",

series = "Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015",

publisher = "Association for Computing Machinery, Inc",

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booktitle = "Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015",

}

Dao, MD, Rangamani, A, Chin, S, Nguyen, NP & Tran, TD 2015, Targeted dot product representation for friend recommendation in online social networks. in J Pei, J Tang & F Silvestri (eds), Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015. Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015, Association for Computing Machinery, Inc, pp. 349-356, IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015, Paris, France, 8/25/15. https://doi.org/10.1145/2808797.2809414

Targeted dot product representation for friend recommendation in online social networks. / Dao, Minh D.; Rangamani, Akshay; Chin, Sang et al.
Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015. ed. / Jian Pei; Jie Tang; Fabrizio Silvestri. Association for Computing Machinery, Inc, 2015. p. 349-356 (Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015).

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

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T1 - Targeted dot product representation for friend recommendation in online social networks

AU - Dao, Minh D.

AU - Rangamani, Akshay

AU - Chin, Sang

AU - Nguyen, Nam P.

AU - Tran, Trac D.

PY - 2015/8/25

Y1 - 2015/8/25

N2 - In this paper, we develop Targeted Dot Product Representation (TarDPR), a DPR-based feature selection and combination framework for friend recommendation in online social networks (OSNs). Our approach modifies conventional DPR techniques and makes itself applicable to OSNs by focusing on computing a consistent representation while minimizing unnecessary suggestions made outside these interested regions. A notable property of TarDPR is its ability to effectively incorporate different types of social features and produce new meaningful features that help competitive approaches to significantly improve their recommendation quality. We derive an iterative algorithm for TarDPR that is supported by mathematical analysis, and is efficient on large social traces. To certify the usability of our approach, we conduct empirical experiments on real social traces including Facebook and Foursquare social networks. The competitive experimental results show that TarDPR achieves up to 15% improvement in comparison with other competitive methods. These results consequently confirm the efficacy of our suggested framework.

AB - In this paper, we develop Targeted Dot Product Representation (TarDPR), a DPR-based feature selection and combination framework for friend recommendation in online social networks (OSNs). Our approach modifies conventional DPR techniques and makes itself applicable to OSNs by focusing on computing a consistent representation while minimizing unnecessary suggestions made outside these interested regions. A notable property of TarDPR is its ability to effectively incorporate different types of social features and produce new meaningful features that help competitive approaches to significantly improve their recommendation quality. We derive an iterative algorithm for TarDPR that is supported by mathematical analysis, and is efficient on large social traces. To certify the usability of our approach, we conduct empirical experiments on real social traces including Facebook and Foursquare social networks. The competitive experimental results show that TarDPR achieves up to 15% improvement in comparison with other competitive methods. These results consequently confirm the efficacy of our suggested framework.

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T3 - Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015

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BT - Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015

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Y2 - 25 August 2015 through 28 August 2015

ER -

Dao MD, Rangamani A, Chin S, Nguyen NP, Tran TD. Targeted dot product representation for friend recommendation in online social networks. In Pei J, Tang J, Silvestri F, editors, Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015. Association for Computing Machinery, Inc. 2015. p. 349-356. (Proceedings of the 2015 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM 2015). doi: 10.1145/2808797.2809414

Targeted dot product representation for friend recommendation in online social networks

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