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Identifying similar people in professional social networks with discriminative probabilistic models

Authors:

Suleyman Cetintas,

Monica Rogati,

Luo Si,

Yi FangAuthors Info & Claims

SIGIR '11: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval

Pages 1209 - 1210

https://doi.org/10.1145/2009916.2010123

Published: 24 July 2011 Publication History

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Abstract

Identifying similar professionals is an important task for many core services in professional social networks. Information about users can be obtained from heterogeneous information sources, and different sources provide different insights on user similarity.

This paper proposes a discriminative probabilistic model that identifies latent content and graph classes for people with similar profile content and social graph similarity patterns, and learns a specialized similarity model for each latent class. To the best of our knowledge, this is the first work on identifying similar professionals in professional social networks, and the first work that identifies latent classes to learn a separate similarity model for each latent class. Experiments on a real-world dataset demonstrate the effectiveness of the proposed discriminative learning model.

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Cited By

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Liu JNg YGui ZSinghal TBlessing LWood KLim K(2022)Title2Vec: a contextual job title embedding for occupational named entity recognition and other applicationsJournal of Big Data10.1186/s40537-022-00649-59:1Online publication date: 3-Sep-2022
https://doi.org/10.1186/s40537-022-00649-5
Constantinov CMocanu M(2021)A Comprehensive Review of Professional Network Impact on Education and CareerChallenges and Applications of Data Analytics in Social Perspectives10.4018/978-1-7998-2566-1.ch001(1-26)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-2566-1.ch001
Liu JNg YWood KLim KBietz MWiggins A(2020)IPOD: A Large-scale Industrial and Professional Occupation DatasetCompanion Publication of the 2020 Conference on Computer Supported Cooperative Work and Social Computing10.1145/3406865.3418329(323-328)Online publication date: 17-Oct-2020
https://dl.acm.org/doi/10.1145/3406865.3418329
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Index Terms

Identifying similar people in professional social networks with discriminative probabilistic models
1. Information systems
  1. Information retrieval

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Information

Published In

SIGIR '11: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval

July 2011

1374 pages

ISBN:9781450307574

DOI:10.1145/2009916

General Chairs:
Wei-Ying Ma
Microsoft Research Asia, China
,
Jian-Yun Nie
University of Montreal, Canada
,
Program Chairs:
Ricardo Baeza-Yates
Yahoo! Research, Spain
,
Tat-Seng Chua
National University of Singapore
,
W. Bruce Croft
University of Massachusetts, Amherst, USA

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 24 July 2011

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SIGIR '11: The 34th International ACM SIGIR conference on research and development in Information Retrieval

July 24 - 28, 2011

Beijing, China

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Overall Acceptance Rate 792 of 3,983 submissions, 20%

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Liu JNg YGui ZSinghal TBlessing LWood KLim K(2022)Title2Vec: a contextual job title embedding for occupational named entity recognition and other applicationsJournal of Big Data10.1186/s40537-022-00649-59:1Online publication date: 3-Sep-2022
https://doi.org/10.1186/s40537-022-00649-5
Constantinov CMocanu M(2021)A Comprehensive Review of Professional Network Impact on Education and CareerChallenges and Applications of Data Analytics in Social Perspectives10.4018/978-1-7998-2566-1.ch001(1-26)Online publication date: 2021
https://doi.org/10.4018/978-1-7998-2566-1.ch001
Liu JNg YWood KLim KBietz MWiggins A(2020)IPOD: A Large-scale Industrial and Professional Occupation DatasetCompanion Publication of the 2020 Conference on Computer Supported Cooperative Work and Social Computing10.1145/3406865.3418329(323-328)Online publication date: 17-Oct-2020
https://dl.acm.org/doi/10.1145/3406865.3418329
Kamkarhaghighi MChepurna IAghababaei SMakrehchi M(2016)Discovering Credible Twitter Users in Stock Market Domain2016 IEEE/WIC/ACM International Conference on Web Intelligence (WI)10.1109/WI.2016.0020(66-72)Online publication date: Oct-2016
https://doi.org/10.1109/WI.2016.0020
Pu WChoi JHwang YAmir E(2015)A deterministic partition function approximation for exponential random graph modelsProceedings of the 24th International Conference on Artificial Intelligence10.5555/2832249.2832276(192-200)Online publication date: 25-Jul-2015
https://dl.acm.org/doi/10.5555/2832249.2832276
Pavan MLee TDe Luca ELindstaedt SLey TSack H(2015)Semantic enrichment for adaptive expert searchProceedings of the 15th International Conference on Knowledge Technologies and Data-driven Business10.1145/2809563.2809621(1-4)Online publication date: 21-Oct-2015
https://dl.acm.org/doi/10.1145/2809563.2809621
Ninawe SVenkataram P(2015)Web Service Query Selection for a Professional Social Network Members2015 9th International Conference on Next Generation Mobile Applications, Services and Technologies10.1109/NGMAST.2015.60(29-34)Online publication date: Sep-2015
https://doi.org/10.1109/NGMAST.2015.60
Xu YLi ZGupta ABugdayci ABhasin AMacskassy SPerlich CLeskovec JWang WGhani R(2014)Modeling professional similarity by mining professional career trajectoriesProceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining10.1145/2623330.2623368(1945-1954)Online publication date: 24-Aug-2014
https://dl.acm.org/doi/10.1145/2623330.2623368
Keyvanpour MMoradi MHasanzadeh F(2014)Digital Forensics 2.0Computational Intelligence in Digital Forensics: Forensic Investigation and Applications10.1007/978-3-319-05885-6_2(17-46)Online publication date: 2014
https://doi.org/10.1007/978-3-319-05885-6_2
Cetintas SSi LXin YTzur RHe QIyengar ANejdl WPei JRastogi R(2013)Probabilistic latent class models for predicting student performanceProceedings of the 22nd ACM international conference on Information & Knowledge Management10.1145/2505515.2507832(1513-1516)Online publication date: 27-Oct-2013
https://dl.acm.org/doi/10.1145/2505515.2507832
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Social professional networks

Building social capital with Facebook: Type of network, availability of other media, and social self-efficacy matter#

Evaluating Effective Use of Social Networks for Recruitment

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Building social capital with Facebook: Type of network, availability of other media, and social self-efficacy matter^#