Abstract
With the popularity of social media, there has been an increasing interest in user profiling and its applications nowadays. This paper presents our system named UIR-SIST for User Profiling Technology Evaluation Campaign in SMP CUP 2017. UIR-SIST aims to complete three tasks, including keywords extraction from blogs, user interests labeling and user growth value prediction. To this end, we first extract keywords from a user's blog, including the blog itself, blogs on the same topic and other blogs published by the same user. Then a unified neural network model is constructed based on a convolutional neural network (CNN) for user interests tagging. Finally, we adopt a stacking model for predicting user growth value. We eventually receive the sixth place with evaluation scores of 0.563, 0.378 and 0.751 on the three tasks, respectively.
Highlights
Social media have recently become an important platform that enables its users to communicate and spread information
The model is based on multi-scale convolutional neural networks with the aim to capture both local and global information for user profiling
The Chinese Software Developer Network (CSDN) data set consists of all user generated content and the behavior data from 157,427 users during 2015, which can be further divided into three parts: 1) 1,000,000 pieces of user blogs, involving blog ID, blog title and the corresponding content; 2) Six types of user behavior data, including posting, browsing, commenting, voting up, voting down and adding favorites, and the corresponding date and time information; 3) Relationship between users, which refers to the records of following and sending private messages
Summary
Social media have recently become an important platform that enables its users to communicate and spread information. The Chinese Software Developer Network (CSDN) is one of the biggest platforms of software. Identifying User Profile by Incorporating Self-Attention Mechanism based on CSDN Data Set developers in China to share technical information and engineering experiences. In SMP CUP 2017 [8], the competition is structured around three tasks based on CSDN blogs : (1) keywords extraction from blogs, (2) user interests labeling and (3) user growth value prediction. Our team from School of Information Science and Technology, University of International Relations participated in all the tasks in User Profiling Technology Evaluation Champaign. A unified neural network model is constructed with self-attention mechanism for task 2. The model is based on multi-scale convolutional neural networks with the aim to capture both local and global information for user profiling.
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