Frontiers of Computer Science

, Volume 11, Issue 6, pp 1085–1097 | Cite as

Personal summarization from profile networks

Research Article

Abstract

Personal profile information on social media like LinkedIn.com and Facebook.com is at the core of many interesting applications, such as talent recommendation and contextual advertising. However, personal profiles usually lack consistent organization confronted with the large amount of available information. Therefore, it is always a challenge for people to quickly find desired information from them. In this paper, we address the task of personal profile summarization by leveraging both textual information and social connection information in social networks from both unsupervised and supervised learning paradigms. Here, using social connection information is motivated by the intuition that people with similar academic, business or social background (e.g., comajor, co-university, and co-corporation) tend to have similar experiences and should have similar summaries. For unsupervised learning, we propose a collective ranking approach, called SocialRank, to combine textual information in an individual profile and social context information from relevant profiles in generating a personal profile summary. For supervised learning, we propose a collective factor graph model, called CoFG, to summarize personal profiles with local textual attribute functions and social connection factors. Extensive evaluation on a large dataset from LinkedIn.com demonstrates the usefulness of social connection information in personal profile summarization and the effectiveness of our proposed unsupervised and supervised learning approaches.

Keywords

natural language processing machine learning social networks personal profile summarization 

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Notes

Acknowledgements

We appreciate Dr. Jie Tang and Dr. Honglei Zhuang for providing their software and useful suggestions about probobility of graph model (PGM). We acknowledge Dr. Xinfang Liu, Dr. Yunxia Xue, and Dr. Yulai Shen for corpus construction and insightful comments.We also thank anonymous reviewers for their valuable suggestions and comments.

The work was supported by the National Natural Science Foundation of China (Grant Nos. 61273320, 61375073, and 61402314) and the Key Project of the National Natural Science Foundation of China (61331011).

Supplementary material

11704_2016_5088_MOESM1_ESM.ppt (212 kb)
Supplementary material, approximately 210 KB.

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Copyright information

© Higher Education Press and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  1. 1.Natural Language Processing Lab, School of Computer Science and TechnologySoochow UniversitySuzhouChina

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