  Globally, recommendation services have become important due to the fact that
they support e-commerce applications and different research communities.
Recommender systems have a large number of applications in many fields
including economic, education, and scientific research. Different empirical
studies have shown that recommender systems are more effective and reliable
than keyword-based search engines for extracting useful knowledge from massive
amounts of data. The problem of recommending similar scientific articles in
scientific community is called scientific paper recommendation. Scientific
paper recommendation aims to recommend new articles or classical articles that
match researchers' interests. It has become an attractive area of study since
the number of scholarly papers increases exponentially. In this survey, we
first introduce the importance and advantages of paper recommender systems.
Second, we review the recommendation algorithms and methods, such as
Content-Based methods, Collaborative Filtering methods, Graph-Based methods and
Hybrid methods. Then, we introduce the evaluation methods of different
recommender systems. Finally, we summarize open issues in the paper recommender
systems, including cold start, sparsity, scalability, privacy, serendipity and
unified scholarly data standards. The purpose of this survey is to provide
comprehensive reviews on scholarly paper recommendation.
