Method for predicting user participation behavior of hot topic by improved RBF neural network

Abstract

The invention discloses a method for predicting a user participation behavior of a hot topic by an improved RBF (Radical Basis Function) neural network, and belongs to the field of computer network information technology analysis. Firstly, the situation that a neural network can take a good fitting effect for a complex nonlinear relationship among user behaviors is considered, and further a user participation behavior prediction model is built by adopting the RBF neural network; secondly, a mapping relationship between a user attribute and the participation behavior has uncertainty, and a cloud theory is introduced for optimizing an activation function of a hidden layer in an RBF; and finally, topic popularity is subjected to exponential function model-based parameter fitting by utilizing time discretization and time slicing methods for a characteristic that the participation behavior of a user is changed with time, so that a topic popularity change trend is obtained.

Description

technical field [0001] The invention belongs to the field of network topic analysis, and in particular relates to user behavior analysis and prediction of hot topics in social networks. Background technique [0002] In recent years, with the continuous popularization and development of the Internet, social networks have increasingly become an important part of many people's lives. Weibo is one of the most representative social networks. It is a social network based on the attention mechanism. The network platform not only allows users to independently choose other users they are interested in to listen to and follow, but also freely publishes their own news. The published news is also broadcast in nature, that is, everyone can see it. It has the function of social network, but also has the nature of media. As a new media of public opinion, the microblog platform has attracted the participation of most netizens in my country, and the hot topics in it have quickly spread to b...

AI Technical Summary

Problems solved by technology

[0005] In the present invention, when predicting the neural network algorithm in the prior art, it is easy to fall into a local minimum and the convergence speed is slow. At the same time, due to the complexity of user behavior, it cannot accurately reflect the fuzziness and randomness between user attributes and user behavior, and the user Participatory behavior changes dynamically over time, etc.

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