期刊：
Environmental Science and Pollution Research,2024年31(12):18448-18464 ISSN：0944-1344
通讯作者：
Xu, SC
作者机构：
[Hao, Meng-Ge; Meng, Xiao-Na; Xu, SC; Xu, Shi-Chun] China Univ Min & Technol, Sch Econ & Management, Xuzhou 221116, Peoples R China.;[Xue, Xiao-Fei] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Peoples R China.
通讯机构：
[Xu, SC ] C;China Univ Min & Technol, Sch Econ & Management, Xuzhou 221116, Peoples R China.
关键词：
Digital economy;Zero-waste city;Waste management;Industrial structure upgrading;Green technology innovation
摘要：
The digital economy is playing a crucial effect in the field of environmental governance. Digital and intelligent management is an essential means to fully realize the "zero-waste city" construction. The present paper investigates the impact of digital economy on China's provincial "zero-waste city" construction. The results indicate that digital economy can contribute to "zero-waste city" construction. The digital economy has a positive nonlinear effect on the construction of "zero-waste city," but the marginal effect is diminishing. The digital economy can facilitate "zero-waste city" construction by improving industrial structure upgrading and green technology innovation. Heterogeneity analysis reveals that digital economy contributes to the construction of "zero-waste city" in the eastern and western regions and high-level environmental regulation regions, while this impact is insignificant in the central region and low-level environmental regulation regions. The digital economy exerts the most significant positive influence on waste resource recycling followed by waste final disposal and then waste reduction at the source. These findings underscore the effect of digital economy in fostering "zero-waste city" construction and promoting sustainable waste management. The present study provides new ideas for the "zero-waste city" construction in emerging developing countries such as China.
作者机构：
[Zheng, Xiaoying; Lu, Quan] Wuhan Univ, Sch Informat Management, Wuhan, Peoples R China.;[Chen, Jing] Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.
会议名称：
IEEE 8th International Conference on Big Data Analytics (ICBDA)
会议时间：
MAR 03-05, 2023
会议地点：
Harbin, PEOPLES R CHINA
会议主办单位：
[Lu, Quan;Zheng, Xiaoying] Wuhan Univ, Sch Informat Management, Wuhan, Peoples R China.^[Chen, Jing] Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.
关键词：
electronic medical record;data mining;clinical treatment pattern;symptomatic treatment
摘要：
This paper analyzed the basic clinical treatment pattern by exploring the relationship between diseases, symptoms and drugs, which help non-medical people understand the basic clinical treatment pattern, so as to better carry out medical and health big data mining and eliminate public prejudice against symptomatic treatment. The FP growth algorithm was used to mine the association rules from EMR big data. Combined with intersection analysis, the basic clinical treatment pattern was summarized. 507 disease-drug rules and 2141 symptom-drug rules were obtained, indicating that both diseases and symptoms are strongly associated with drugs. Intersection analysis showed that 33.7% of the disease-drug rules were symptom-independent, while 34.6% of the symptom-drug rules were disease-independent. The basic clinical treatment pattern consists of three parts: (1) The combination of disease and symptomatic medication pattern. (2) Independent disease medication pattern. (3) Independent symptomatic medication pattern.
作者机构：
[Ye, Yi; Deng, Qiumiao; Ma, Xiao; Yang, Tingting] Zhongnan Univ Econ & Law, Sch Informat & Safety Engn, Wuhan, Peoples R China.;[Zeng, Jiangfeng] Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.
会议名称：
25th IEEE International Conference on Computational Science and Engineering (CSE)
会议时间：
DEC 09-11, 2022
会议地点：
Wuhan, PEOPLES R CHINA
会议主办单位：
[Ma, Xiao;Deng, Qiumiao;Ye, Yi;Yang, Tingting] Zhongnan Univ Econ & Law, Sch Informat & Safety Engn, Wuhan, Peoples R China.^[Zeng, Jiangfeng] Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.
会议论文集名称：
IEEE International Conference on Computational Science and Engineering
关键词：
Co-author Recommendation;Heterogeneous Information Networks;Meta-path based context;Attention Mechanism
摘要：
In real academic networks, there exist multiple types of entities(authors, papers, terms, conferences) and links between them. Therefore, the academic networks are generally considered as heterogeneous information networks(HINs). Existing collaborator recommendation methods in heterogeneous networks are generally based on the embeddings of nodes and links with respect to some given meta-paths. However, they seldom learn meta-paths representations which can provide important interaction information. What's more, the impact of different meta-paths on recommendation are neglected. In order to deal with these unsolved problems, we propose an attention based collaborator recommendation method in the setting of heterogeneous academic networks. Firstly, we select some meta-paths according to the HIN schema. Secondly, the embeddings of nodes and meta-path instances are generated by employing the Skip-gram and Convolutional Neural Network(CNN) models respectively. Thirdly, the attention mechanism is devised to integrate the multiple sources of embeddings so as to produce the author representations and meta-path based context representations. Finally, the Multi-Layer Perceptron is utilized for recommendation task. Comparative experiments conducted on the DBLP dataset demonstrate the effectiveness of our proposed method.
作者机构：
[Jiang, Hui] Shandong Inst Business & Technol, Sch Management Sci & Engn, Yantai, Peoples R China.;[Zhu, Yongdi; Jiang, Hui; Duan, Yaoqing] Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.
会议名称：
10th International Conference on Big Data (BigData) Held as Part of the 19th Services Conference Federation (SCF)
会议时间：
DEC 10-14, 2021
会议地点：
ELECTR NETWORK
会议主办单位：
[Jiang, Hui] Shandong Inst Business & Technol, Sch Management Sci & Engn, Yantai, Peoples R China.^[Jiang, Hui;Duan, Yaoqing;Zhu, Yongdi] Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.
会议论文集名称：
Lecture Notes in Computer Science
关键词：
Continuous-use intention;Open data;Open government data
摘要：
An improved understanding of the factors that influence citizens' continuance use intention will help to promote and improve the practice of open government data. This paper constructs an integrated model that provides insight into factors that influence citizens' continued intention to use open government data. The model contains 296 effective samples from questionnaires, which are then tested by the Structural Equation Model. It is found that perceived usefulness and satisfaction significantly affect the public's continuous adoption of OGD; expectation confirmation significantly affects satisfaction and perceived usefulness, and thus indirectly affects the public's continuous adoption of OGD; perceived ease of use significantly affects the satisfaction, trust in government and trust in the Internet significantly affects the expectation confirmation. But perceived usefulness, trust in government and trust in the Internet had no significant effect on public satisfaction.
作者机构：
[Chi, Maomao; Ma, Haiyan] China Univ Geosci, Sch Econ & Management, Wuhan, Peoples R China.;[Wang, Yunran] Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.
会议名称：
3rd International Conference on Design, Operation and Evaluation of Mobile Communications (MOBILE) Held as Part of 24th International Conference on Human-Computer Interaction (HCII)
会议时间：
JUN 26-JUL 01, 2022
会议地点：
ELECTR NETWORK
会议主办单位：
[Chi, Maomao;Ma, Haiyan] China Univ Geosci, Sch Econ & Management, Wuhan, Peoples R China.^[Wang, Yunran] Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.
会议论文集名称：
Lecture Notes in Computer Science
关键词：
Mobile games;Uses and gratifications theory;Stickiness
摘要：
Despite the huge growth potential that has been predicted for mobile game continuous usage intention, little is known about what motives users to be sticky under the mobile game context. Drawing on the Uses and Gratifications theory (UGT), this study aims to investigate the influencing effects of players' characteristics and the mobile game structures on players' mobile game behavior (e.g. stickiness). After surveying 439 samples, the research model is tested with Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that both individual gratifications and mobile game presence positively affect users' stickiness. Furthermore, we find that leisure boredom of individual situations and integration of mobile game governance positively affect users' stickiness. The results provide further insights into the design and governance strategies of mobile games.
摘要：
Image classification plays a significant role in robotic vision. This paper proposes an image classification model: Xception-LightGBM, which combines with Xception and Light Gradient Boosting Machine for hybrid image classification. The proposed algorithm produces the image feature extraction via Xception and classifies these feature vectors using Light Gradient Boosting Machine (LightGBM). The Xception-LightGBM model is compared with five representative image prediction models, such as VGG16, VGG19, InceptionV3, DenseNet121, and Xception. The experiments on six data sets demonstrate this proposed model leads to successful runs and provides optimal performances. It shows this model achieves the best results for all six evaluation metrics: accuracy, precision, recall, F1-Score, loss, and Jaccard. Furthermore, this proposed model acquires the highest accuracy on six image data sets, which has at least 1.1% in accuracy improved to the Xception architecture. It suggests this model may be preferable for robotic vision.
摘要：
Social media data are used to enhance crisis management, as people widely adopt social media to share and acquire information to cope with uncertainties in crises. Identification and extraction of informative communications out of large volumes of data is critical for accurate situational awareness and timely response. Existing studies use conditions of geolocations, keywords, and topics separately or jointly to retrieve data that can be crisis related, but are not enough to filter subsets of data for different crisis management tasks. We propose that the crisis communication purposes of users can be detected to enhance data selection and prioritization for different crisis management tasks. A classification framework was built to identify three facets of a message: content type, audience type, and information source. The definitions of these categories are not dependent on a specific type of crises. So the classification framework can be potentially applied to different crisis scenarios. Machine learning models were created for the automatic classification of messages. Results showed the CNN-based model achieved the best accuracy (88.5%) for the classification of content type. The proposed Naive Bayes and logistic repression with predetermined features can best differentiate audience types and information source with an accuracy of 72.7% and 72.2%, respectively.
作者机构：
[Wang, Wei] WISDRI Engn & Res Inc Ltd, Res & Dev Inst, Wuhan, Peoples R China.;[Feng, Changyang] Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.;[Quan, Wei] Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USA.
会议名称：
IEEE International Conference on Big Data (Big Data)
会议时间：
DEC 09-12, 2019
会议地点：
Los Angeles, CA
会议主办单位：
[Wang, Wei] WISDRI Engn & Res Inc Ltd, Res & Dev Inst, Wuhan, Peoples R China.^[Feng, Changyang] Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.^[Quan, Wei] Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USA.
会议论文集名称：
IEEE International Conference on Big Data
摘要：
The study presented in this poster deals with the model-free optimal synchronization control of the complex dynamical networks (CDNs) with unknown non-identical dynamics. The traditional synchronization control methods of the CDNs require the complete knowledge of system dynamics. However, there usually exists substantive data which contains the information of system state variables. In addition, we are surrounded by big data with the advent of rapid and low-cost data acquisition techniques. We can employ the data-driven method to acquire the control law. Thus, in this study, we propose a data-driven model-free optimal control scheme based on reinforcement learning (RL) to achieve the synchronization of the CDNs. First, a data-driven adaptive distributed observer is designed to estimate the reference node state for each node. A feedforward control law is designed to compensate the coupling dynamics of the CDNs. With the help of the adaptive distributed observer and the feedforward control law, the synchronization control of the CDNs transforms into the optimal control problem of an augmented systems, which is composed of the system dynamics with compensation and the reference node dynamics. Then, a model-free RL-based control method using measurable data is developed to solve the optimal control problem. Finally, the simulation results are provided to demonstrate the effectiveness of the developed approach.
作者机构：
[Zhou, Ke; Jiang, Tianming] Huazhong Univ Sci & Technol, Sch Comp Sci & Technol, Engn Res Ctr Data Storage Syst & Technol,Minist E, Wuhan Natl Lab Optoelect,Key Lab Informat Storage, Wuhan, Peoples R China.;[Zeng, Jiangfeng] Cent China Normal Univ, Sch Informat Management, Wuhan, Peoples R China.;[Huang, Ping] Temple Univ, Philadelphia, PA 19122 USA.;[Huang, Ping] Huanghuai Univ, Zhumadian, Peoples R China.
会议名称：
37th IEEE International Conference on Computer Design (ICCD)
会议时间：
NOV 17-20, 2019
会议地点：
New York Univ Abu Dhabi, Abu Dhabi, U ARAB EMIRATES
会议主办单位：
New York Univ Abu Dhabi
会议论文集名称：
Proceedings IEEE International Conference on Computer Design
关键词：
disk failure;data reliability;SMART;adversarial training;anomaly detection
摘要：
As a classical technique in storage systems, disk failure prediction aims at predicting impending disk failures in advance for high data reliability. Over the past decades, taking as input the SMART (Self-Monitoring, Analysis and Reporting Technology) attributes, many supervised machine learning algorithms have been proven to be effective for disk failure prediction. However, these approaches heavily rely on the availability of substantial annotated failed disk data which unfortunately exhibits an extreme data imbalance, i.e., the number of failed disks is much smaller than that of healthy ones, resulting in suboptimal performance and even inability at the beginning of their deployment, i.e., cold starting problem. Inspired by the significant success achieved in GAN (Generative Adversarial Network) based anomaly detection, in this paper, we translate disk failure prediction into an anomaly detection problem. Specifically, we develop a novel Semi-supervised method for lifelong disk failure Prediction via Adversarial training, called SPA. The distinguishing feature of SPA from existing supervised approaches is that SPA is only trained on healthy disks, which avoids the traditional limitations of imbalance in datasets and eliminates the cold starting problem. Furthermore, a novel 2D image-like representation technique is proposed to enable the deployment of deep learning techniques and the automatic feature extraction. Experimental results on real-world SMART datasets demonstrate that, compared with the state-of-the-art supervised machine learning based methods, our approach predicts disk failures at a higher accuracy for the entire lifetime of models, i.e., both the initial period and the long-term usage.
作者：
Guo, Zhen*;Wang, Zengfu;Hu, Yumei;Pan, Quan;Zhang, Jun
作者机构：
[Pan, Quan; Guo, Zhen; Hu, Yumei; Wang, Zengfu] Northwestern Polytech Univ, Sch Automat, Xian, Shaanxi, Peoples R China.;[Zhang, Jun] Cent China Normal Univ, Sch Informat Management, Wuhan, Hubei, Peoples R China.
会议名称：
21st International Conference on Information Fusion (FUSION)
会议时间：
JUL 10-13, 2018
会议地点：
Cambridge, ENGLAND
会议主办单位：
[Guo, Zhen;Wang, Zengfu;Hu, Yumei;Pan, Quan] Northwestern Polytech Univ, Sch Automat, Xian, Shaanxi, Peoples R China.^[Zhang, Jun] Cent China Normal Univ, Sch Informat Management, Wuhan, Hubei, Peoples R China.
关键词：
OTHR target tracking;ionospheric model;Gaussian Markov random field
摘要：
This paper proposes a new virtual ionospheric height model for over-the-horizon radar (OTHR) target tracking. Considering the spatial correlation of different ionosphere site, the virtual ionospheric heights are modeled by a Gaussian Markov random field (GMRF). The priors of the GMRF model can be learned from the historical measurements from ionosondes. Given the acquired measurements of the ionosphere subregions, the virtual ionospheric heights of the unmeasured subregions are inferred based on the GMRF model. Then we present the multipath probabilistic data association for uncertain coordinate registration (MPCR) with the new virtual ionospheric height model. Numerical simulation shows that the accuracy of OTHR target tracking is improved.
作者机构：
[Zhu, Qiang] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Hubei, Peoples R China.;[Jiang, Xingpeng; He, Tingting; Hu, Xiaohua; Pan, Min; Zhu, Qing] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.;[Hu, Xiaohua] Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USA.
通讯机构：
[He, Tingting] C;Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.
会议名称：
IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Human Genomics
会议时间：
DEC 03-06, 2018
会议地点：
Madrid, SPAIN
会议主办单位：
[Zhu, Qiang] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Hubei, Peoples R China.^[Zhu, Qing;Pan, Min;Jiang, Xingpeng;Hu, Xiaohua;He, Tingting] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.^[Hu, Xiaohua] Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USA.
会议论文集名称：
IEEE International Conference on Bioinformatics and Biomedicine-BIBM
摘要：
Microorganisms are closely related to human health and have an impact on the development of various diseases. It is extremely significant to identify the relationships between microorganisms and the phenotypes (such as healthy or disease status) by analyzing microbial abundance in personalized medicine. Deep learning allows computational models that composed of multiple processing layers to learn representation of data with multiple levels of abstraction. These methods have improved the state-of-the-art in speech recognition, visual object recognition and object detection. However, current deep models are typically neural networks which are actually multiple layers of parameterized differentiable nonlinear models that can be trained by backpropagation. It is interesting to explore other deep learning models to handle tasks with small sample size and high dimensional data. While a unique feature of microbial data is that it has phylogenetic tree structure information which can be embedded to improve the classification performance. In this work, in order to further improve the metagenomic classification, we propose a deep model named Cascade Deep Forest which keeps the spatial structure between nodes through embedding phylogenetic tree information. Our results demonstrate: 1) the modified cascade structure can enhance the classification performance of Deep Forest; 2) embedding phylogenetic tree information can also improve the classification of the models; 3) Deep Forest achieves highly competitive performance to deep neural networks.
摘要：
Location-inventory problem has long been a classic area of research on the integrated optimization of logistics system, the previous research about LIP focused on forward logistics network without taking the reverse logistics into account. In recent years, the explosive growth of ecommerce and frequently returns in the e-commerce activities makes that we have to consider the LIP with returns. Therefore, we complete the formulization of this problem as a nonlinear integer programming model and, and an effective heuristic algorithm based on the improved genetic algorithm is used to solve the model. Computational results show the validity and advancement of the model and algorithm.
摘要：
Dynamic network is drawing more and more attention due to its potential in capturing time-dependent phenomena such as online public opinion and biological system. Microbial interaction networks that model the microbial system are often dynamic, static analysis methods are difficult to obtain reliable knowledge on evolving communities. To fulfill this gap, a dynamic clustering approach based on evolutionary symmetric nonnegative matrix factorization (ESNMF) is used to analyze the microbiome time-series data. To our knowledge, this is the first attempt to extract dynamic modules across time-series microbial interaction network. ESNMF systematically integrates temporal smoothness cost into the objective function by simultaneously refining the clustering structure in the current network and minimizing the clustering deviation in successive timestamps. We apply the proposed framework on a human microbiome datasets from infants delivered vaginally and ones born via C-section. The proposed method cannot only identify the evolving modules related to certain functions of microbial communities, but also discriminate differences in two kinds of networks obtained from infants delivered vaginally and via C-section.
作者机构：
[Zhu, Qiang] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Hubei, Peoples R China.;[Jiang, Xingpeng; He, Tingting; Hu, Xiaohua; Pan, Min; Hu, XH; Liu, Lei; Li, Bojing] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.;[Hu, Xiaohua] Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USA.
会议名称：
IEEE International Conference on Bioinformatics and Biomedicine (BIBM) - Human Genomics
会议时间：
DEC 03-06, 2018
会议地点：
Madrid, SPAIN
会议主办单位：
[Zhu, Qiang] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Hubei, Peoples R China.^[Pan, Min;Liu, Lei;Li, Bojing;He, Tingting;Jiang, Xingpeng;Hu, Xiaohua] Cent China Normal Univ, Sch Comp, Wuhan 430079, Hubei, Peoples R China.^[Hu, Xiaohua] Drexel Univ, Coll Comp & Informat, Philadelphia, PA 19104 USA.
会议论文集名称：
IEEE International Conference on Bioinformatics and Biomedicine-BIBM
摘要：
With the rapid advancement of DNA sequencing, metagenomics and metatranscriptomics have made great progress, which deepen our understanding on the human microbiome and its impact on human health and diseases. The microbiome, which is characterized by small samples, high dimensions and complicated relationships with hosts, refers to the species, genes and genomes of the microbiota, as well as the products of the microbiota and the host environment. In fact, many machine learning methods have been used to conduct Microbiome-Wide Association Studies which can link the microbiome with the phenotypes, such as the status of human health and diseases. However, existing methods such as Support Vector Machines (SVMs) have some limitations on deep representation learning with deep architectures which can promote the reuse of features and potentially lead to progressively more abstract features at higher layers of representations. Recently, Deep Neural Networks (DNNs), a kind of deep learning models, are widely used for metagenomic data analysis and can perform well on representation learning. But they are considered as a black box and sufferring from criticisms due to theirs lacking of interpretability. Thus, it is interesting to explore other deep learning models for metagenomic data analysis. In this work, we introduce a deep learning model called Deep Forest to study the microbiome associations and we also present an ensemble method for feature selection. Experimental results show that Deep Forest outperforms the traditional machine learning methods. In addition, compared to DNNs, Deep Forest has better interpretability and less hyperparameters.
作者机构：
[Wu, Jianhua; Wang, Jiaojiao; Li, Meijuan] Cent China Normal Univ, Sch Informat Management, Wuhan 430079, Hubei, Peoples R China.
会议名称：
18th IEEE International Conference on Advanced Learning Technologies (ICALT)
会议时间：
JUL 09-13, 2018
会议地点：
Indian Inst Technol Bombay, Bombay, INDIA
会议主办单位：
Indian Inst Technol Bombay
会议论文集名称：
IEEE International Conference on Advanced Learning Technologies
关键词：
website design;information literacy training;pyramid model of information literacy educational game;mini encyclopedia of information literacy;educational game
摘要：
According to a pyramid model of information literacy educational game proposed by the research team, a website, Information Literacy Training, was set up, in which a series of mini-games are organized at a transparent pyramid, and a mini encyclopedia is also built for players to resolve doubts. The website has been published and provided as learning resources for students. The design idea, function and structure of the website are introduced in this paper.
会议名称：
6th International Conference on Health Information Science (HIS)
会议时间：
OCT 07-09, 2017
会议地点：
Moscow Inst Phys & Technol, Moscow, RUSSIA
会议主办单位：
Moscow Inst Phys & Technol
会议论文集名称：
Lecture Notes in Computer Science
关键词：
Personal Health Record;Multidimensional modelling;Ontology;HL7;openEHR
摘要：
Personal Health Records (PHRs) have characteristics of continuous high speed growth and rich value, which are the prerequisite and foundation for implementing services of intelligent health care, personalized medicine, remote treatment, disease prevention and prediction, and the strong support for the hospital, health care institutions, insurance companies and other organizations to maintain personal health. PHR contents have multidimensional features such as time, region, population and role orientation, which have different semantic meaning and application value. As the fundamental element of semantic web technology architecture, ontology provides an expressive framework for reusing, sharing, representing and reasoning knowledge, and has been widely applied in modelling biological, medicine and health care fields. This paper analyzes the multidimensional features of PHRs, and investigates an approach for modelling PHRs based on current existing health record standards by using ontology modelling methods and theoretical frameworks.
作者机构：
[Xiong, Hui-xiang; Yang, Xu; Hu, Jin] Cent China Normal Univ, Dept Informat Management, Wuhan 430079, Hubei, Peoples R China.
通讯机构：
[Xiong, Hui-xiang] C;Cent China Normal Univ, Dept Informat Management, Wuhan 430079, Hubei, Peoples R China.
会议名称：
2nd International Conference on Computer Science and Applications (CSA)
会议时间：
NOV 20-22, 2015
会议地点：
Wuhan, PEOPLES R CHINA
会议主办单位：
[Xiong, Hui-xiang;Hu, Jin;Yang, Xu] Cent China Normal Univ, Dept Informat Management, Wuhan 430079, Hubei, Peoples R China.
关键词：
Microblog sequencing;Text mining;User interest model
摘要：
This paper firstly analyzes drawbacks of sequencing in microblog, and then proposed a ranking model based on contents to optimize the information flow of microblog. This model extracts characteristics which reflect users' interests from their microblog contents by text mining, and combines their tags to construct interest model for them, then calculates and ranks the weight of users' information flow in their homepage in terms of the user interest model to help users preferentially scan the microblog content which they interested in mostly. After verified via R programming and a real example, this model is effective.
