Computer Science ›› 2020, Vol. 47 ›› Issue (2): 51-57.doi: 10.11896/jsjkx.190300121

• Database & Big Data & Data Science • Previous Articles     Next Articles

Academic Paper Recommendation Method Combined with Researcher Tag

WU Lei1,YUE Feng2,WANG Han-ru3,WANG Gang3   

  1. (Personnel Department,Hefei University of Technology,Hefei 230009,China)1;
    (School of Computer Science and Information Engineering,Hefei University of Technology,Hefei 230009,China)2;
    (School of Management,Hefei University of Technology,Hefei 230009,China)3
  • Received:2019-03-25 Online:2020-02-15 Published:2020-03-18
  • About author:WU Lei,born in 1979,Ph.D,doctorial student,lecturer.His main research interests include recommender system and so on.
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (71471054, 91646111), Ministry of Education of Humanities and Social Science Foundation (18YJC870025) and Natural Science Foundation of Anhui Province, China (1608085MG150).

Abstract: In recent years,the rise of scientific social networks has changed the original mode of exchanges and cooperation among researchers to some extent,which makes scientific social networks well received by researchers.With the surge of research fin-dings on scientific social networks,it’s difficult for researchers to find research papers they are really interested in.Consequently,it becomes an important task to recommend the papers that researchers are interested in.Considering the particularity of resear-chers’ reading data,this paper conducted paper recommen-dation from the perspective of one class collaborative filtering.On the one hand,researchers’ tag information is used to extract negative cases precisely;on the other hand,based on the researcher-paper matrix with negative instances incorporated,the researchers-tag matrix and papers’ similarity information are jointly integrated into the probability matrix factorization,to alleviate the data sparsity problem.Finally,experiments were carried out on a scientific social network,ScholarMate.Four evaluation metrics,namely precision,recall,MAP,and MRR,were adopted to verify the recommendation accuracy as well as the recommendation order.The experimental results show that the proposed method performs betterthan the baselines with an improvement of 4.19% in terms of the precision,which demonstrate the effectiveness of considering the paper recommendation on scientific social networks as a one-class collaborative filtering problem,the effectiveness of introducing extra social information to improve the recommendation results,and the scalability of the proposed method.

Key words: Scientific social networks, Paper recommendation, One class collaborative filtering, Researcher tag, Probabilistic matrix factorization

CLC Number: 

  • TP391.3
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