Paper Info
Title
C-RBFNN: A user retweet behavior prediction method for hotspot topics based on improved RBF neural network.
Abstract
The prediction of user behavior plays an important role in perceiving the popularity of a topic and the changes of public opinion on the Internet. In this paper, focusing on user retweeting behavior, we propose a user retweeting prediction method for hotspot topics using fuzzy theory and neural network algorithm. Firstly, RBF (Radical Basis Function) neural network is used to model users retweeting behavior, considering that neural network can be effective in simulating the non-linear relationships among complex behaviors of users. Compared with traditional neural networks, an RBF neural network has the advantages of fast convergence and local approximation to eigenvalues when dealing with large - scale network topic data and does not suffer from the problems posed by local minimum. Besides, time-decay function is introduced to make RBF neural network can adapt to dynamic changes of various influence factors in the social networks. Secondly, we introduce cloud theory in fuzzy mathematics to optimize the activation function of hidden layers of RBF neural network because of the uncertainty of the mapping between user attributes and retweeting behavior, and then we propose the C-RBF neural network (Cloud-RBFNN), which makes the method cannot only fully express the fuzziness and randomness of user retweeting behavior, but also has good approximation ability for nonlinear relationships. Finally, because the characteristics of user retweeting behavior can change over time, changes of topic development trend are obtained by using a discrete-time method and analyzing the number of users who participate in a topic in different time periods. Experiments show that the method can accurately predict the user retweeting behavior and dynamically perceive changes in hotspot topics.
Year
DOI
Venue
2018
10.1016/j.neucom.2017.09.015
Neurocomputing
Keywords
Field
DocType
Hotspot topic,RBF neural network,Fuzzy theory,User behavior prediction
Convergence (routing),Computer science,Activation function,Fuzzy logic,Fuzzy mathematics,Artificial intelligence,Artificial neural network,Hotspot (Wi-Fi),Machine learning,Cloud computing,The Internet
Journal
Volume
ISSN
Citations 
275
0925-2312
6
PageRank 
References 
Authors
0.44
20
3
Name
Order
Citations
PageRank
Yanbing Liu115516.38
Jinzhe Zhao260.44
Yunpeng Xiao33310.88