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As science advances, the academic community has published millions ofresearch papers. Researchers devote time and effort to search relevantmanuscripts when writing a paper or simply to keep up with current research. Inthis paper, we consider the problem of citation recommendation by extending aset of known-to-be-relevant references. Our analysis shows the degrees of citedpapers in the subgraph induced by the citations of a paper, called projectiongraph, follow a power law distribution. Existing popular methods are only goodat finding the long tail papers, the ones that are highly connected to others.In other words, the majority of cited papers are loosely connected in theprojection graph but they are not going to be found by existing methods. Toaddress this problem, we propose to combine author, venue and keywordinformation to interpret the citation behavior behind those loosely connectedpapers. Results show that different methods are finding cited papers withwidely different properties. We suggest multiple recommended lists by differentalgorithms could satisfy various users for a real citation recommendationsystem. Moreover, we also explore the fast local approximation for combinedmethods in order to improve the efficiency.

Towards Finding Non-obvious Papers: An Analysis of Citation Recommender Systems