  Citation recommendation is an important task to assist scholars in finding
candidate literature to cite. Traditional studies focus on static models of
recommending citations, which do not explicitly distinguish differences between
papers that are caused by temporal variations. Although, some researchers have
investigated chronological citation recommendation by adding time related
function or modeling textual topics dynamically. These solutions can hardly
cope with function generalization or cold-start problems when there is no
information for user profiling or there are isolated papers never being cited.
With the rise and fall of science paradigms, scientific topics tend to change
and evolve over time. People would have the time preference when citing papers,
since most of the theoretical basis exist in classical readings that published
in old time, while new techniques are proposed in more recent papers. To
explore chronological citation recommendation, this paper wants to predict the
time preference based on user queries, which is a probability distribution of
citing papers published in different time slices. Then, we use this time
preference to re-rank the initial citation list obtained by content-based
filtering. Experimental results demonstrate that task performance can be
further enhanced by time preference and it's flexible to be added in other
citation recommendation frameworks.
