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Personal summarization from profile networks

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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.

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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).

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Authors and Affiliations

  1. Natural Language Processing Lab, School of Computer Science and Technology, Soochow University, Suzhou, 215006, China

    Zhongqing Wang, Shoushan Li & Guodong Zhou

Authors
  1. Zhongqing Wang
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  2. Shoushan Li
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  3. Guodong Zhou
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Corresponding author

Correspondence to Guodong Zhou.

Additional information

Zhongqing Wang received the Master’s degree in July 2012 from the School of Computer Science and Technology, Soochow University, China. Since 2012, he has been a PhD candidate at the School of Computer Science and Technology, Soochow University. His current research interests include natural language processing, sentiment analysis, and social computing.

Shoushan Li received his PhD degree in 2008 from National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Science, China. He is a full professor in the School of Computer Science and Technology, Soochow University, China. His current research interests include natural language processing, social computing, and sentient analysis.

Guodong Zhou received his PhD degree in 1999 from the National University of Singapore, Singapore. He is a full professor in the School of Computer Science and Technology, and the director of the Natural Language Processing Laboratory from Soochow University, China. His research interests include information retrieval and natural language processing.

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Wang, Z., Li, S. & Zhou, G. Personal summarization from profile networks. Front. Comput. Sci. 11, 1085–1097 (2017). https://doi.org/10.1007/s11704-016-5088-3

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