  The ever-growing number of venues publishing academic work makes it difficult
for researchers to identify venues that publish data and research most in line
with their scholarly interests. A solution is needed, therefore, whereby
researchers can identify information dissemination pathways in order to both
access and contribute to an existing body of knowledge. In this study, we
present a system to recommend scholarly venues rated in terms of relevance to a
given researcher's current scholarly pursuits and interests. We collected our
data from an academic social network and modeled researchers' scholarly reading
behavior in order to propose a new and adaptive implicit rating technique for
venues. We present a way to recommend relevant, specialized scholarly venues
using these implicit ratings that can provide quick results, even for new
researchers without a publication history and for emerging scholarly venues
that do not yet have an impact factor. We performed a large-scale experiment
with real data to evaluate the current scholarly recommendation system and
showed that our proposed system achieves better results than the baseline. The
results provide important up-to-the-minute signals that compared with
post-publication usage-based metrics represent a closer reflection of a
researcher's interests.
