  Academic administrators and funding agencies must predict the publication
productivity of research groups and individuals to assess authors' abilities.
However, such prediction remains an elusive task due to the randomness of
individual research and the diversity of authors' productivity patterns. We
applied two kinds of approaches to this prediction task: deep neural network
learning and model-based approaches. We found that a neural network cannot give
a good long-term prediction for groups, while the model-based approaches cannot
provide short-term predictions for individuals. We proposed a model that
integrates the advantages of both data-driven and model-based approaches, and
the effectiveness of this method was validated by applying it to a high-quality
dblp dataset, demonstrating that the proposed model outperforms the tested
data-driven and model-based approaches.
