| 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. |
| 关键词: 群组推荐 社交网络 隐含信息 矩阵分解 因子图模型 |
| DOI：10.13328/j.cnki.jos.005267 |
| 分类号: |
| 基金项目:国家自然科学基金（61331011，61375073，61402314） |
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| Personal Group Recommendation via Textual and Social Information |
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WANG Zhong-Qing, LI Shou-Shan, ZHOU Guo-Dong
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Natural Language Processing Laboratory, School of Computer Science and Technology, Soochow University, Suzhou 215006, China
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| 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. |
| Key words: group recommendation social network implicit information matrix factorization factor graph model |
