CASIA OpenIR  > 模式识别国家重点实验室  > 多媒体计算
项连城1,2; 方全1,2; 桑基韬1,2; 徐常胜1,2; 路冬媛3
Source Publication软件学报
Other AbstractInferring user attributes is important for user profiling, retrieval, and personalization. Most existing work infers user attribute independently and ignores the relations between attributes. In this work, a new method is proposed to infer user attributes via hypergraph learning. In the hypergragh, each vertex represents a user in the social media, and the hyperedges are used to capture the similarity relations of the user generated content and the relations between attributes. The user attributes inference is formalized into a regularization label similar propagation problem in the constructed hypergraph, which can effectively infer the users’ various attributes. Extensive experiments conducted on a collected dataset from Google+ with full attribute annotations demonstrate the effectiveness of the proposed approach in user attribute inference.
Keyword超图 用户属性挖掘 属性关系
Document Type期刊论文
Corresponding Author路冬媛
Affiliation1.模式识别国家重点实验室(中国科学院 自动化研究所),北京 100190
2.China-Singapore Institute of Digital Media, Singapore 119615
3.National University of Singapore, Singapore 119615
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
项连城,方全,桑基韬,等. 基于社交媒体的关联性用户属性推断[J]. 软件学报,2015,26(Suppl.(2)):145-154.
APA 项连城,方全,桑基韬,徐常胜,&路冬媛.(2015).基于社交媒体的关联性用户属性推断.软件学报,26(Suppl.(2)),145-154.
MLA 项连城,et al."基于社交媒体的关联性用户属性推断".软件学报 26.Suppl.(2)(2015):145-154.
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