10.1145/2467696.2467743acmconferencesArticle/Chapter ViewAbstractPublication PagesjcdlConference Proceedingsconference-collections
research-article

Can't see the forest for the trees?: a citation recommendation system

Online:22 July 2013Publication History

ABSTRACT

Scientists continue to find challenges in the ever increasing amount of information that has been produced on a world wide scale, during the last decades. When writing a paper, an author searches for the most relevant citations that started or were the foundation of a particular topic, which would very likely explain the thinking or algorithms that are employed. The search is usually done using specific keywords submitted to literature search engines such as Google Scholar and CiteSeer. However, finding relevant citations is distinctive from producing articles that are only topically similar to an author's proposal. In this paper, we address the problem of citation recommendation using a singular value decomposition approach. The models are trained and evaluated on the Citeseer digital library. The results of our experiments show that the proposed approach achieves significant success when compared with collaborative filtering methods on the citation recommendation task.

References

  1. S. Bethard and D. Jurafsky. Who should i cite? learning literature search models from citation behavior. In Proceedings of the 19th ACM international conference on Information and knowledge management, CIKM '10, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. Journal of Machine Learning Research, 3:993--1022, 2003. Google ScholarGoogle ScholarDigital LibraryDigital Library
  3. S. Deerwester, S. T. Dumais, G. W. Furnas, T. K. Landauer, and R. Harshman. Indexing by latent semantic analysis. Journal of the American Society for Information Science, 41(6):391--407, 1990.Google ScholarGoogle Scholar
  4. C. L. Giles, K. Bollacker, and S. Lawrence. Citeseer: An automatic citation indexing system. In Digital Libraries '98, pages 89--98, 1998. Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. Google. Google scholar. In http://scholar.google.com.Google ScholarGoogle Scholar
  6. Q. He, J. Pei, D. Kifer, P. Mitra, and C. L. Giles. Context-aware citation recommendation. In Proceedings of the 19th international conference on World Wide Web '10, pages 421--430, 2010. Google ScholarGoogle ScholarDigital LibraryDigital Library
  7. W. Huang, S. Kataria, C. Caragea, P. Mitra, C. L. Giles, and L. Rokach. Recommending citations: Translating papers into references. In Proceedings of the 21st ACM CIKM '12, 2012. Google ScholarGoogle ScholarDigital LibraryDigital Library
  8. S. Kataria, P. Mitra, and S. Bhatia. Utilizing context in generative bayesian models for linked corpus. In Proceeding of AAAI, 2010.Google ScholarGoogle Scholar
  9. Y. Lu, J. He, D. Shan, and H. Yan. Recommending citations with translation model. In Proceedings of CIKM '11, pages 2017--2020, 2011. Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. S. M. McNee, I. Albert, D. Cosley, P. Gopalkrishnan, S. K. Lam, A. M. Rashid, J. A. Konstan, and J. Riedl. On the recommending of citations for research papers. In Proceedings of the 2002 ACM conference on Computer supported cooperative work '02, pages 116--125, 2002. Google ScholarGoogle ScholarDigital LibraryDigital Library
  11. R. M. Nallapati, A. Ahmed, E. P. Xing, and W. W. Cohen. Joint latent topic models for text and citations. In Proc. of KDD, pages 542--550, 2008. Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Application of dimensionality reduction in recommender system a case study. In WebKDD-2000 Workshop, 2000.Google ScholarGoogle ScholarCross RefCross Ref
  13. P. Smolensky. Parallel distributed processing: explorations in the microstructure of cognition. volume 1, pages 194--281. 1986. Google ScholarGoogle ScholarDigital LibraryDigital Library
  14. T. Strohman, W. B. Croft, and D. Jensen. Recommending citations for academic papers. IR 466, 2006.Google ScholarGoogle Scholar
  15. J. Tang and J. Zhang. A discriminative approach to topic-based citation recommendation. In Proceedings of the 13th PAKDD '09, pages 572--579, 2009. Google ScholarGoogle ScholarDigital LibraryDigital Library
  16. S. Teufel, A. Siddharthan, and D. Tidhar. Automatic classification of citation function. In Proceedings of EMNLP-06, 2006. Google ScholarGoogle ScholarDigital LibraryDigital Library
  17. B. Webb. Netflix update: Try this at home. In http://sifter.org/$\sim$simon/journal/20061211.html, 2006.Google ScholarGoogle Scholar

Index Terms

  1. Can't see the forest for the trees?: a citation recommendation system

      Comments

      Login options

      Check if you have access through your login credentials or your institution to get full access on this article.

      Sign in

      PDF Format

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader
      About Cookies On This Site

      We use cookies to ensure that we give you the best experience on our website.

      Learn more

      Got it!