Co-citation Proximity Analysis

[1][2] The similarity measure builds on the co-citation analysis approach, but differs in that it exploits the information implied in the placement of citations within the full-texts of documents.

Therefore, the CPA approach allows for the calculation of a more granular resolution of overall document similarity.

CPA has been found to outperform co-citation analysis, especially when documents contain extensive bibliographies and in cases where documents have not been frequently cited together (i.e. have a low co-citation score).

Identifying Related Documents For Research Paper Recommender By CPA And COA.

In S. I. Ao, C. Douglas, W. S. Grundfest, and J. Burgstone, editors, Proceedings of the world congress on engineering and computer science 2009, volume 1 of Lecture Notes in Engineering and Computer Science, pages 636-639, Berkeley (USA), oct 2009. International Association of Engineers (IAENG), Newswood Limited.

In M. Lalmas, J. Jose, A. Rauber, F. Sebastiani, and I. Frommholz, editors, Proceedings of the 14th European conference on digital libraries (ecdl’10): research and advanced technology for digital libraries, volume 6273 of Lecture Notes of Computer Science (LNCS).

Documents B and C are cited in closer proximity to each other in the full-text of the citing document, when compared to document A. Hence, according to co-citation proximity analysis, documents B and C are more strongly related than documents A and B or A and C.
Figure visualizing the Co-citation Proximity Analysis (CPA) approach to document similarity computation.