基于社交媒体的关联性用户属性推断

挖掘用户属性对用户建模、用户检索和个性化服务等具有十分重要的意义.已有的相关研究工作都是单独挖掘各种属性,而且忽略了各属性之间的相关关系.提出一种基于超图学习的用户属性推断的方法.在超图中,顶点表示社会媒体中的用户,超边表示用户产生的内容相似性与属性之间的关系.在建好的超图模型上,把用户属性挖掘形式化成一个正则化的标签相似传播问题,可以有效推断得到用户的各种属性.利用从Google+上收集的标记过全部属性的数据集进行了大量的实验,其结果表明了该方法在用户属性挖掘中的有效性.

Exploiting Social Media Information for Relational User Attribute Inference

Inferring 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.