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OTT Messages Modeling and Classification Based on Recurrent Neural Networks 被引量:3

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摘要 A vast amount of information has been produced in recent years,which brings a huge challenge to information management.The better usage of big data is of important theoretical and practical significance for effectively addressing and managing messages.In this paper,we propose a nine-rectangle-grid information model according to the information value and privacy,and then present information use policies based on the rough set theory.Recurrent neural networks were employed to classify OTT messages.The content of user interest is effectively incorporated into the classification process during the annotation of OTT messages,ending with a reliable trained classification model.Experimental results showed that the proposed method yielded an accurate classification performance and hence can be used for effective distribution and control of OTT messages.
出处 《Computers, Materials & Continua》 SCIE EI 2020年第5期769-785,共17页 计算机、材料和连续体(英文)
基金 This work is supported by the Research on Big Data in Application for Education of BUPT(No.2018Y0403) Fundamental Research Funds of BUPT(No.2018XKJC07,2018RC27) the National Natural Science Foundation of China(No.61571059).
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