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Research Topics Variation Analysis and Prediction Based on FARO and Neural Networks
Zhu, Hongyin1; Zeng, Yi1,2; Yiping Yang1
2016-10
Conference Name2016 IEEE International Conference on Systems, Man, and Cybernetics
Conference DateOctober 9-12, 2016
Conference PlaceHungary
Publication PlaceHungary
PublisherIEEE International Conference on Systems, Man, and Cybernetics
AbstractGiven the explosive growth of scientific information and the fast advancement of research fields, researchers may not be able to find the most promising topics to combine with their current research and may be trapped in a few familiar research topics without creative ideas. Many studies of recommendation system make the effort to address the above problem, but they ignore the different styles of users and generate the recommendation results based on a common strategy. In this paper, we propose a framework to generate the adaptive recommendation results according to the research styles of users. Our framework contains 3 main parts, the research topic ontology construction, trend prediction and recommendation. First of all, the Fun of Academic Research Ontology (FARO), which has the capacity of describing dynamic and static research features and building a social network, is constructed to organize entities about academic research. Secondly, this paper predicts the popularity variation of research topics with the neural network model. Finally, some adaptive topics are recommended to specific researchers according to the evaluation of their research styles. Basically, this paper is inspired by the associative thinking of human brain to combine the advantages of Web knowledge representation language and the neural network to execute the prediction and recommendation. We test our results based on the publication data of IEEE and Springer. The experimental results demonstrate that our prediction model has a good generalization performance. A questionnaire survey is carried out to assess the recommendation results, and the result shows the feasibility of our method.
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14353
Collection类脑智能研究中心
Affiliation1.Institute of Automation, Chinese Academy of Sciences, Beijing, China
2.Center for Excellence in Brain Science and Intelligence Technology, Chinese Academy of Sciences, Shanghai, China
Recommended Citation
GB/T 7714
Zhu, Hongyin,Zeng, Yi,Yiping Yang. Research Topics Variation Analysis and Prediction Based on FARO and Neural Networks[C]. Hungary:IEEE International Conference on Systems, Man, and Cybernetics,2016.
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