硕士生李裕礞论文被Cognitive Computation (COGN)录用
新闻来源:IR实验室       发布时间:2018/9/1 21:26:03

  近日收到期刊Cognitive Computation (COGN)编辑部邮件,实验室硕士生李裕礞的论文“Improving User Attribute Classification with Text and Social Network Attention”被录用。Cognitive Computation (COGN)为情感认知领域期刊,该期刊MedSci指数为2.4884,SCI期刊分区为医学3区、行为科学 2区。

  论文中英文摘要如下:

  User attribute classification is an important research topic in social media user profiling, which has great commercial value in modern advertisement systems. Existing research on user profiling has mostly focused on manually handcrafted features for different attribute classification tasks. However, these research has partly overlooked the social relation of users.

  We propose an end-to-end neural network model called the social convolution attention neural network. Our model leverages the convolution attention mechanism to automatically extract user features with respect to different attributes from social texts. The proposed model can capture the social relation of users by combining semantic context and social network information, and improve the performance of attribute classification. We evaluate our model in the gender, age and geography classification tasks based on the dataset from SMP CUP 2016 competition, respectively.

  The experimental results demonstrate that the proposed model is effective in automatic user attribute classification with a particular focus on fine-grained user information. We propose an effective model based on the convolution attention mechanism and social relation information for user attribute classification. The model can significantly improve the accuracy in various user attribute classification tasks.


  用户属性分类是社会媒体用户分析的一个重要研究课题,在现代广告系统中具有很大的商业价值。现有的用户概要研究主要集中在针对不同的属性分类任务手工创建的特性上。然而,这些研究部分忽视了用户的社会关系。

  我们提出了一种端到端的神经网络模型,称为社会卷积注意神经网络。我们的模型利用卷积注意机制,从社会文本中自动提取不同属性的用户特征。该模型将语义语境和社会网络信息结合起来,能够捕捉用户的社会关系,提高属性分类的性能。我们分别基于来自SMPCUP2016比赛的数据,对我们的性别、年龄和地理分类任务进行了评估。

  实验结果表明,该模型对用户属性的自动分类具有较好的效果,特别关注细粒度用户信息。提出了一种基于卷积注意机制和社会关系信息的用户属性分类方法。该模型能显著提高各种用户属性分类任务的准确率。