Purpose

The purpose of this paper is to solve the problems existing in topic popularity prediction in online social networks and advance a fine-grained and long-term prediction model for lack of sufficient data.

Design/methodology/approach

Based on GM(1,1) and neural networks, a co-training model for topic tendency prediction is proposed in this paper. The interpolation based on GM(1,1) is employed to generate fine-grained prediction values of topic popularity time series and two neural network models are considered to achieve convergence by transmitting training parameters via their loss functions.

Findings

The experiment results indicate that the integrated model can effectively predict dense sequence with higher performance than other algorithms, such as NN and RBF_LSSVM. Furthermore, the Markov chain state transition probability matrix model is used to improve the prediction results.

Practical implications

Fine-grained and long-term topic popularity prediction, further improvement could be made by predicting any interpolation in the time interval of popularity data points.

Originality/value

The paper succeeds in constructing a co-training model with GM(1,1) and neural networks. Markov chain state transition probability matrix is deployed for further improvement of popularity tendency prediction.

中文翻译：

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基于灰色理论的BP-NN协同训练用于密集序列长期趋势预测

目的

本文的目的是解决在线社交网络中主题流行度预测中存在的问题，并在缺乏足够数据的情况下，提出一种细粒度且长期的预测模型。

设计/方法/方法

基于GM（1,1）和神经网络，提出了一种主题趋势预测的协同训练模型。利用基于GM（1,1）的插值生成主题流行度时间序列的细粒度预测值，并考虑了两个神经网络模型通过经由其损失函数传输训练参数来实现收敛。

发现

实验结果表明，与NN，RBF_LSSVM等算法相比，该集成模型能够有效地预测稠密序列，并且具有更高的性能。此外，使用马尔可夫链状态转移概率矩阵模型来改善预测结果。

实际影响

细粒度且长期的主题受欢迎程度预测，可以通过预测受欢迎程度数据点的时间间隔中的任何插值来进行进一步的改进。

创意/价值

本文成功地构建了带有GM（1,1）和神经网络的协同训练模型。马尔可夫链状态转移概率矩阵用于进一步改善流行趋势预测。