Abstract: In recent years,the number of scientific papers in scientific social networks has grown at an explosive rate.It is difficult for researchers to find scientific papers related to their research.Therefore,the paper recommendation for researchers was proposed to solve this problem.However,many problems exist in traditional paper recommendation methods,especially for the fact that a lot of social information in scientific social network are not fully used,resulting in poor quality of paper recommendation.Therefore,this research proposed a new paper recommendation method for researchers in scientific social networks based on the unified probability matrix factorization.This method incorporates social tag information and group information into traditional matrix factorization.In order to verify the validity of the proposed method,we crawled data from a famous scientific social network,i.e.CiteULike,to conduct experiments.Experimental results show that the proposed method gets the best recommendation results at the two evaluation metrics,i.e. Precision and Recall,compared to other traditional recommendation methods.The proposed method is linear with respect to the number of observed data,and performs well in scalability.

Key words: Scientific paper recommendation,Scientific social network,Unified probabilistic matrix factorization,Recommendation method