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Adaptively temporal graph convolution model for epidemic prediction of multiple age groups

Fundamental Research 2022年第2期2卷 311-320页

作者： Yuejiao Wang Dajun Daniel Zeng Qingpeng Zhang Pengfei Zhao Xiaoli Wang Quanyi Wang Yin Luo Zhidong Cao The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijingChina School of Artificial Intelligence University of Chinese Academy of SciencesBeijingChina Shenzhen Artificial Intelligence and Data Science Institute(Longhua) ShenzhenChina School of Data Science City University of Hong Kong-Hong Kong SARChina Institute for Infectious Disease and Endemic Disease Control Beijing Center for Disease Prevention and Control BeijingChina

Introduction:Multivariate time series prediction of infectious diseases is significant to public health,and the deep learning method has attracted increasing attention in this research *** and methods:An adaptively temporal graph convolution(ATGCN)model,which leams the contact patterns of multiple age groups in a graph-based approach,was proposed for COVID-19 and influenza *** compared ATGCN with autoregressive models,deep sequence learning models,and experience-based ATGCN models in short-term and long-term prediction ***:Results showed that the ATGCN model performed better than the autoregressive models and the deep sequence learning models on two datasets in both short-term(12.5%and 10%improvements on RMSE)and longterm(12.4%and 5%improvements on RMSE)prediction *** the RMSE of ATGCN predictions fluctuated least in different age groups of COVID-19(0.029±0.003)and influenza(0.059±0.008).Compared with the Ones-ATGCN model or the Pre-ATGCN model,the ATGCN model was more robust in performance,with RMSE of 0.0293 and 0.06 on two datasets when horizon is ***:Our research indicates a broad application prospect of deep learning in the field of infectious disease *** characteristics and domain knowledge of infectious diseases should be further applied to the design of deep learning models and feature ***:The ATGCN model addressed the multivariate time series forecasting in a graph-based deep learning approach and achieved robust prediction on the confirmed cases of multiple age groups,indicating its great potentials for exploring the implicit interactions of multivariate variables.

关键词： graph convolution model Infectious disease prediction Multiple age group Multivariate time series Public health

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Item Recommendation Based on Monotonous Behavior Chains

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国际计算机前沿大会会议论文集 2021年第1期 17-31页

作者： Tiepai Yang Heilongjiang University Harbin 150080HeilongjiangChina

In the field of e-commerce,recommendation systems can accurately provide users with products and services of potential interest,thereby enhancing users’online shopping experience.“Explicit”feedback and“implicit”feedback are mostly studied in two relatively independent research *** the actual interaction process between users and commodities,there is a kind of signal between the two Monotonic dependence,that is,sparse and reliable explicit signals must imply dense and noisy implicit *** this paper,a special“monotonic behavior chain”structure is proposed to constrain the two signals,and a series of usercommodity interaction behaviors is mapped into a user-commodity multi-stage binary interaction *** two feedback signals were combined and the complete interaction was simulated between the user and the *** a depth model framework GAERE was proposed based on the graph auto-encoder,which converts the matrix completion problem of the traditional recommendation system into the problem of graph link *** realistic data sets were applied to evaluate the effectiveness of the proposed *** model shows competitiveness on standard collaborative filtering *** addition,the application of graph convolutional network was further explored to process graph structure data in recommendation system from the perspective of user behavior intention.

关键词： Recommendation system graph convolution model Matrix completion

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