@Article{cmc.2023.040095,
AUTHOR = {Guohui Song, Yongbin Wang, Jianfei Li, Hongbin Hu},
TITLE = {Predicting the Popularity of Online News Based on the Dynamic Fusion of Multiple Features},
JOURNAL = {Computers, Materials \& Continua},
VOLUME = {76},
YEAR = {2023},
NUMBER = {2},
PAGES = {1621--1641},
URL = {http://www.techscience.com/cmc/v76n2/54009},
ISSN = {1546-2226},
ABSTRACT = {Predicting the popularity of online news is essential for news providers and recommendation systems. Time series, content and meta-feature are important features in news popularity prediction. However, there is a lack of exploration of how to integrate them effectively into a deep learning model and how effective and valuable they are to the model’s performance. This work proposes a novel deep learning model named Multiple Features Dynamic Fusion (MFDF) for news popularity prediction. For modeling time series, long short-term memory networks and attention-based convolution neural networks are used to capture long-term trends and short-term fluctuations of online news popularity. The typical convolution neural network gets headline semantic representation for modeling news headlines. In addition, a hierarchical attention network is exploited to extract news content semantic representation while using the latent Dirichlet allocation model to get the subject distribution of news as a semantic supplement. A factorization machine is employed to model the interaction relationship between meta-features. Considering the role of these features at different stages, the proposed model exploits a time-based attention fusion layer to fuse multiple features dynamically. During the training phase, this work designs a loss function based on Newton’s cooling law to train the model better. Extensive experiments on the real-world dataset from Toutiao confirm the effectiveness of the dynamic fusion of multiple features and demonstrate significant performance improvements over state-of-the-art news prediction techniques.},
DOI = {10.32604/cmc.2023.040095}
}