基于深度神经网络的多因素感知终端换机预测模型

针对基于特征工程的传统终端换机预测模型依赖于领域知识且无法充分利用用户通话、流量使用等序列数据的问题，提出基于深度神经网络的多因素融合终端换机预测模型. 该模型使用长短时记忆网络（LSTM）提取用户通话、流量使用行为序列特征，使用全连接网络融合用户自然属性、行为序列特征和历史换机信息，预测用户是否换机. 实验表明，基于深度神经网络的多因素融合终端换机预测模型能够考虑影响用户换机的多种因素，充分挖掘用户通话、流量使用行为序列特征；当召回率为0.135时，相比于传统模型精确率提高了34.3%.

Prediction model of multi-factor aware mobile terminal replacement based on deep neural network

A multi-factor aware mobile terminal replacement prediction model based on deep neural networks was proposed to address the problem that traditional mobile terminal replacement prediction models based on feature engineering rely on the domain knowledge and cannot sufficiently use user’s call details and data traffic details. Long short-term memory (LSTM) networks were utilized to extract the sequence characteristics of user’s call and data traffic behaviors. Then a fully connected neural network was utilized to fuse user’s natural attributes, sequence characteristics, and historical terminal replacement information for prediction. The experimental results show that the proposed model can consider multiple factors affecting terminal replacement and sufficiently exploit the sequence characteristics of user’s call details and data traffic details. The precision was increased by 34.3% compared with traditional methods when recall was set to 0.135.