基于深度学习的用户投诉预测模型研究

发布时间：2018-03-27 11:46

  本文选题：电信投诉预测　切入点：深度学习　出处：《计算机应用研究》2017年05期

【摘要】：用户投诉预测模型能有效地降低电信用户投诉率,对企业提高用户满意度和竞争力有着至关重要的作用。在模型训练过程中,由于人工设计特征的缺陷和设计过程中存在难以预估的复杂性,使得模型预测的精度和设计特征的效率不能有很大的提升。针对上述问题,提出了一种基于深度学习的用户投诉预测模型。该模型通过深层网络特征学习单元能从电信用户原始数据中自动学习到适合分类器分类的高层非线性组合特征,并将这些高层特征输入到传统分类器中来提高模型的精度。通过实验结果分析,预测模型在AUC指标上比以往用户投诉模型提升了7.1%,证明了该模型自动学习特征的有效性和深度学习在电信大数据领域的可用性。
[Abstract]:The customer complaint prediction model can effectively reduce the rate of telecom users' complaints and play an important role in improving the customer satisfaction and competitiveness of enterprises. Because of the defects of the artificial design features and the complexity of the design process, the precision of the model prediction and the efficiency of the design features can not be greatly improved. In this paper, a user complaint prediction model based on deep learning is proposed, which can automatically learn high-level nonlinear composite features suitable for classifier classification from the original data of telecom users through the deep network feature learning unit. These high-level features are input into the traditional classifier to improve the accuracy of the model. Compared with the previous user complaint model, the predictive model improves the AUC index by 7.1, which proves the validity of the model's automatic learning feature and the usability of the in-depth learning in the field of telecom big data.
【作者单位】： 苏州大学计算机科学与技术学院;香港城市大学创意媒体学院;
【基金】：国家自然科学基金资助项目(61373092,61033013,61272449,61202029) 江苏省教育厅重大项目(12KJA520004) 江苏省科技支撑计划重点项目(BE2014005)
【分类号】：F274;F626;TP181
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本文编号：1671388