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Contact

Norman Meuschke

Data & Knowledge Engineering Group
University of Wuppertal
School of Electrical,
Information and Media Engineering
Rainer-Gruenter-Str. 21
D-42119 Wuppertal
Office: FC 1.19

Phone: +49 (0)202 439 1618

meuschke{at}uni-wuppertal.de

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Co-Citation Proximity Analysis - Recommendation and Clustering Algorithms for Academic Literature

Co-Citation Proximity Analysis (CPA) is a method to compute both local and global instances of semantic similarity in academic documents by examining citation proximity in the full texts of documents. 

CPA was developed with two applications in mind: recommender systems and clustering. Regarding the first application, an improved measure of document semantic similarity, which computes similarity at a more fine-grained resolution, has the potential to significantly improve the relevance of academic literature recommendations. Regarding the second application, a more granular measure of document similarity allows the development of more precise clustering algorithms for academic literature.

The CPA approach is an advancement of the well-known and widespread co-citation analysis. However, in addition to co-citation analysis, CPA was the first approach that proposed using modified weights based on the proximity of co-citations to each other within an article's full text. The underlying idea is that the closer citations are to each other in the full-text of documents, the more likely they are related.

In comparison to existing approaches, like bibliographic coupling, co-citation analysis or keyword-based similarity computations, CPA achieves a higher precision and offers the possibility to pinpoint related chapters, sections or paragraphs within the texts of academic documents. Moreover, CPA allows a more precise automatic document classification.

RELATED PUBLICATIONS

  1. Citolytics – A Wikipedia Recommender System
    M. Schwarzer, C. Breitinger, M. Schubotz, N. Meuschke, and B. Gipp
    in Proceedings of the 11th ACM Conference on Recommender Systems (RecSys), 2017
    PDF
  2. Evaluating Link-based Recommendations for Wikipedia
    M. Schwarzer, M. Schubotz, N. Meuschke, C. Breitinger, V. Markl, and B. Gipp
    in Proceedings of the 16th ACM/IEEE-CS Joint Conference on Digital Libraries (JCDL), New York, NY, USA, 2016, pp. 191-200
    PDFDOI
  3. A New Approach for Identifying Related Work Based on Co-Citation Analysis
    B. Gipp and J. Beel
    in Proceedings of the 12th International Conference on Scientometrics and Informetrics (ISSI’09), Rio de Janeiro, Brazil, 2009
    PDF

MEDIA COVERAGE

The Wikimedia Research Newsletter, a monthly online overview of recent academic research about Wikipedia and other Wikimedia projects, reports on our paper "Evaluating Link-based Recommendations for...

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zuletzt bearbeitet am: 16.04.2019