  The literature search has always been an important part of an academic
research. It greatly helps to improve the quality of the research process and
output, and increase the efficiency of the researchers in terms of their novel
contribution to science. As the number of published papers increases every
year, a manual search becomes more exhaustive even with the help of today's
search engines since they are not specialized for this task. In academics, two
relevant papers do not always have to share keywords, cite one another, or even
be in the same field. Although a well-known paper is usually an easy pray in
such a hunt, relevant papers using a different terminology, especially recent
ones, are not obvious to the eye.
  In this work, we propose paper recommendation algorithms by using the
citation information among papers. The proposed algorithms are direction aware
in the sense that they can be tuned to find either recent or traditional
papers. The algorithms require a set of papers as input and recommend a set of
related ones. If the user wants to give negative or positive feedback on the
suggested paper set, the recommendation is refined. The search process can be
easily guided in that sense by relevance feedback. We show that this slight
guidance helps the user to reach a desired paper in a more efficient way. We
adapt our models and algorithms also for the venue and reviewer recommendation
tasks. Accuracy of the models and algorithms is thoroughly evaluated by
comparison with multiple baselines and algorithms from the literature in terms
of several objectives specific to citation, venue, and reviewer recommendation
tasks. All of these algorithms are implemented within a publicly available
web-service framework (http://theadvisor.osu.edu/) which currently uses the
data from DBLP and CiteSeer to construct the proposed citation graph.
