Research Paper Recommendation Using Citation Proximity Analysis in Bibliographic Coupling
The immense proliferation of research papers in journals and conferences poses challenges for researchers wanting to access relevant scholarly papers. Recommender systems offer a solution to this research problem by filtering all of the available information and delivering what is most relevant to the user.
Several approaches have been proposed for research paper recommendation, variously based on metadata, content, citation analysis, collaborative filtering, etc. Approaches predicated on citation analysis, including co-citation analysis and bibliographic coupling, have proven to be significant. Co-citation has been analyzed at content level and the use of citation proximity analysis has shown significant improvement in accuracy. However, co-citation presents the relationship between two papers based on their having been mutually cited by other papers, without considering the contents of the citing papers. Bibliographic coupling, on the other hand, considers two papers as relevant if they share common references, but traditionally does not consider the citing patterns of common references in different logical parts of the citing papers.
The improvement found in cases of co-citation when combined with content analysis, motivated us to analyze the impact of using proximity analysis of in-text citations in cases of bibliographic coupling. Therefore, in this research, three different approaches were proposed that extended bibliographic coupling by exploiting the proximity of in-text citations of bibliographically coupled articles. These approaches are: (1) DBSCAN-based bibliographic coupling, (2) centiles-based bibliographic coupling and (3) section based bibliographic coupling. Comprehensive experiments utilizing both user study and automated evaluations were conducted to evaluate the proposed approaches. The results showed significant improvement over traditional bibliographic coupling and content-based research paper recommendation.