ISSN 1000-1239 CN 11-1777/TP

Journal of Computer Research and Development ›› 2020, Vol. 57 ›› Issue (8): 1755-1768.doi: 10.7544/issn1000-1239.2020.20200217

Special Issue: 2020数据挖掘与知识发现专题

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A Hierarchical Attention Mechanism Framework for Internet Credit Evaluation

Chen Yanmin1,2, Wang Hao1, Ma Jianhui1, Du Dongfang3, Zhao Hongke4   

  1. 1(School of Computer Science and Technology, University of Science and Technology of China, Hefei 230027);2(School of Computer Science and Technology, Xinjiang Normal University, Urumqi 830054);3(Tencent Inc, Beijing 100080);4(College of Management and Economics, Tianjin University, Tianjin 300072)
  • Online:2020-08-01
  • Supported by: 
    This work was supported by the National Natural Science Foundation of China (U1605251, 61727809, 61562087), the Scientific Program of the Higher Education Institution of Xinjiang (XJEDU2016S068), and the Xinjiang Normal University Key Laboratory Project (XJNUSYS102018B03).

Abstract: With the development of the Internet, online service products based on user credit have been increasingly applied to various fields. The Internet user credit data, which contains diverse types of data, describes the user’s various aspects. Thus how to use user’s data to evaluate users’ credit ratings on the Internet is an important issue. Most of previous research methods mainly focus on the traditional credit evaluation which is based on the extraction of attributes in the credit field. However, there are only a few of work on Internet credit evaluation. And those work lies in lacking efficient methods to consider the different importance of multiple user attributes on their credit history. Therefore, to solve these problems, this paper presents a hierarchical attention mechanism framework for user credit evaluation based on users’ profiles. Specifically, first, the model builds user profile with user attributes such as user credit history and user behaviors to describe the coarse granularity of users. Then, the significance of user’s attribute with multiple attention layers is gradually obtained to achieve the evaluation of user credit ratings. Extensive experimental results on the public dataset have demonstrated that this model can achieve better performance on evaluation of user than other benchmark algorithms.

Key words: attention mechanism, hierarchical neural network, user credit evaluation, credit level, feature extraction

CLC Number: