基于用户行为序列特征的位置预测模型

针对现有位置预测研究中忽略用户行为序列特性、预测精度提升受限的问题,提出了基于用户行为序列特征的位置预测模型.首先以人工提取的方式构建用户行为的序列特征,融合到位置预测模型中,构造了基于行为序列特征的循环神经网络模型（BCP-RNN）;借助RNN模型循环结构的特点,自动学习行为序列特征,并引入位置预测模型,构造了3层对称循环神经网络模型（TS-RNN）.实验结果证明,引入行为序列特征的BCP-RNN和TS-RNN模型,其预测性能均高于现有的位置预测模型,验证了行为序列特征对挖掘用户移动模式的重要性.相较于人工提取行为序列特征的BCP-RNN模型,TS-RNN不仅节省了人工特征提取的成本,还弥补了人工分析的片面性造成的偏差,具有更高的预测性能.

Location Prediction Model Based on User Behavior Sequence Features

In order to solve the problem of ignoring the character of user behavior sequence and limiting the improvement of prediction accuracy, two location prediction models based on the character of user behavior sequence were proposed. Firstly, behavior+context+profile+RNN (BCP-RNN) model is constructed by manually extracting sequence features of user behaviors and integrating the features into the location prediction model. Then three-layer symmetrical neural network (TS-RNN) model is constructed by automatically learning behavior sequence features based on the recurrent structure of RNN model and integrating the features into location prediction model. Experiments show that, compared with the existing location prediction models, BCP-RNN and TS-RNN improves the prediction performance, verifying the importance of behavior sequence features in mining user movement patterns. Besides, compared with the BCP-RNN model of manually extracting behavior sequence features, TS-RNN not only saves the cost of artificial feature extraction, but also makes up for the deviation caused by one-sided human analysis, and has higher prediction accuracy.