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Journal of Software:2017.28(9):2468-2480

基于文本与社交信息的用户群组识别
王中卿,李寿山,周国栋
(苏州大学 计算机科学与技术学院 自然语言处理实验室, 江苏 苏州 215006)
Personal Group Recommendation via Textual and Social Information
WANG Zhong-Qing,LI Shou-Shan,ZHOU Guo-Dong
(Natural Language Processing Laboratory, School of Computer Science and Technology, Soochow University, Suzhou 215006, China)
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Received:January 29, 2016    Revised:February 17, 2017
> 中文摘要: 社交媒体上的个人群体信息对于理解社交网络结构非常有用,现有研究主要基于用户之间的链接和显式社交信息识别用户的个人群体,很少考虑使用文本信息与隐含社交信息.在显式社交信息缺乏时,隐含社交信息以及文本信息对于识别用户的群体是非常有帮助的.提出一种隐含因子图模型,有效地利用各种隐含与显式的社交与文本信息对用户的群组进行识别.其中,显式的文本与社交信息是通过用户发表的文本与个人关系生成的.同时,利用矩阵分解模型自动生成隐含的文本与社交信息.最后,利用因子图模型与置信传播算法对显式与隐含的文本与社交信息进行集成,并对用户群组识别模型进行学习与预测.实验结果表明,该方法能够有效地对用户群组进行识别.
Abstract:Personal group information on social media is useful for understanding social structures. Existing studies mainly focus on detecting personal groups using explicit social information between users, but few pay attention on using implicit social information and textual information. In this paper, a latent factor graph model (LFGM) is proposed to recommend personal groups for each person with both explicit and implicit information from textual content and social context. Especially, while explicit textual and social contents can be easily extracted from user generated content and personal friendship information, a matrix factorization approach is applied to generate both implicit textual and social information. Evaluation on a large-scale dataset validates the effectiveness of the proposed approach.
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基金项目:国家自然科学基金(61331011,61375073,61402314) 国家自然科学基金(61331011,61375073,61402314)
Foundation items:National Natural Science Foundation of China (61331011, 61375073, 61402314)
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王中卿,李寿山,周国栋.基于文本与社交信息的用户群组识别.软件学报,2017,28(9):2468-2480

WANG Zhong-Qing,LI Shou-Shan,ZHOU Guo-Dong.Personal Group Recommendation via Textual and Social Information.Journal of Software,2017,28(9):2468-2480