Data Enhanced Behavior Prediction Algorithm Based on Information Network Structure
FU Chen-bo1，2，XIA Yi-nan1，2，YUE Xin-chen1，2，YU Shan-qing2，MIN Yong2
1（College of Information Engineering，Zhejiang University of Technology，Hangzhou 310000，China）2（Institute of Cyberspace Security，Zhejiang University of Technology，Hangzhou 310000，China）
Abstract：With the rapid increase of user mobile data on the Internet，the prediction of user′s mobility behavior has become a hot spot for prediction research.In recent years，the recurrent neural network（RNN） technology has been widely used in mobile prediction because of its high efficiency and scalability.However，most of the mobile behavior data collected from the Internet are sparse and heterogeneous，especially when users may refuse to submit activity records to the platform for privacy reasons.Therefore，RNN based prediction technology can not effectively learn enough user behavior features on these sparse data sets，which affects the prediction performance of the model.To solve this problem，this paper proposes a data-enhanced behavior prediction algorithm based on information network structure.Specifically，firstly，we transform the user′s historical behavior data into an information network graph； secondly，we evaluate the information transmission efficiency of users through the modularity of the information network； finally，we sample the user′s friend data according to the information transmission efficiency，and embed the friend data with high information transmission efficiency into the user data to enhance the user data.The experimental results on the real yelp dataset show that our method can enhance the existing algorithm models，and the prediction performance of all models has been greatly improved.
傅晨波,夏镒楠,岳昕晨,俞山青,闵勇. 一种融合信息网络结构的数据增强行为预测算法[J]. 小型微型计算机系统, 2022, 43(3): 568-573.
FU Chen-bo,XIA Yi-nan,YUE Xin-chen,YU Shan-qing,MIN Yong. Data Enhanced Behavior Prediction Algorithm Based on Information Network Structure. Journal of Chinese Computer Systems, 2022, 43(3): 568-573.
2022, Vol. 43 
