Personal profile information on social media like LinkedIn.com and Facebook.com is at the core of many inter- esting applications, such as talent recommendation and con- textual advertising. However, personal profiles...Personal profile information on social media like LinkedIn.com and Facebook.com is at the core of many inter- esting applications, such as talent recommendation and con- textual 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 connec- tion information is motivated by the intuition that people with similar academic, business or social background （e.g., co- major, co-university, and co-corporation） tend to have similar experiences and should have similar summaries. For unsu- pervised learning, we propose a collective ranking approach, called SocialRank, to combine textual information in an in- dividual profile and social context information from relevant profiles in generating a personal profile summary. For super- vised learning, we propose a collective factor graph model, called CoFG, to summarize personal profiles with local tex- tual attribute functions and social connection factors. Exten- sive evaluation on a large dataset from LinkedIn.com demon- strates the usefulness of social connection information in per- sonal profile summarization and the effectiveness of our pro- posed unsupervised and supervised learning approaches.展开更多
基金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）.
文摘Personal profile information on social media like LinkedIn.com and Facebook.com is at the core of many inter- esting applications, such as talent recommendation and con- textual 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 connec- tion information is motivated by the intuition that people with similar academic, business or social background （e.g., co- major, co-university, and co-corporation） tend to have similar experiences and should have similar summaries. For unsu- pervised learning, we propose a collective ranking approach, called SocialRank, to combine textual information in an in- dividual profile and social context information from relevant profiles in generating a personal profile summary. For super- vised learning, we propose a collective factor graph model, called CoFG, to summarize personal profiles with local tex- tual attribute functions and social connection factors. Exten- sive evaluation on a large dataset from LinkedIn.com demon- strates the usefulness of social connection information in per- sonal profile summarization and the effectiveness of our pro- posed unsupervised and supervised learning approaches.
