  The rapid evolution of scientific research has been creating a huge volume of
publications every year. Among the many quantification measures of scientific
impact, citation count stands out for its frequent use in the research
community. Although peer review process is the mainly reliable way of
predicting a paper's future impact, the ability to foresee lasting impact on
the basis of citation records is increasingly important in the scientific
impact analysis in the era of big data. This paper focuses on the long-term
citation count prediction for individual publications, which has become an
emerging and challenging applied research topic. Based on the four key
phenomena confirmed independently in previous studies of long-term scientific
impact quantification, including the intrinsic quality of publications, the
aging effect and the Matthew effect and the recency effect, we unify the
formulations of all these observations in this paper. Building on a foundation
of the above formulations, we propose a long-term citation count prediction
model for individual papers via recurrent neural network with long short-term
memory units. Extensive experiments on a real-large citation data set
demonstrate that the proposed model consistently outperforms existing methods,
and achieves a significant performance improvement.
