  A huge number of academic papers are coming out from a lot of conferences and
journals these days. In these circumstances, most researchers rely on key-based
search or browsing through proceedings of top conferences and journals to find
their related work. To ease this difficulty, we propose a Personalized Academic
Research Paper Recommendation System, which recommends related articles, for
each researcher, that may be interesting to her/him. In this paper, we first
introduce our web crawler to retrieve research papers from the web. Then, we
define similarity between two research papers based on the text similarity
between them. Finally, we propose our recommender system developed using
collaborative filtering methods. Our evaluation results demonstrate that our
system recommends good quality research papers.
