Location via proxy:   [ UP ]  
[Report a bug]   [Manage cookies]                
skip to main content
10.1145/2467696.2467743acmconferencesArticle/Chapter ViewAbstractPublication PagesjcdlConference Proceedingsconference-collections
research-article

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

Published: 22 July 2013 Publication 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.
[2]
D. M. Blei, A. Y. Ng, and M. I. Jordan. Latent dirichlet allocation. Journal of Machine Learning Research, 3:993--1022, 2003.
[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.
[4]
C. L. Giles, K. Bollacker, and S. Lawrence. Citeseer: An automatic citation indexing system. In Digital Libraries '98, pages 89--98, 1998.
[5]
Google. Google scholar. In http://scholar.google.com.
[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.
[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.
[8]
S. Kataria, P. Mitra, and S. Bhatia. Utilizing context in generative bayesian models for linked corpus. In Proceeding of AAAI, 2010.
[9]
Y. Lu, J. He, D. Shan, and H. Yan. Recommending citations with translation model. In Proceedings of CIKM '11, pages 2017--2020, 2011.
[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.
[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.
[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.
[13]
P. Smolensky. Parallel distributed processing: explorations in the microstructure of cognition. volume 1, pages 194--281. 1986.
[14]
T. Strohman, W. B. Croft, and D. Jensen. Recommending citations for academic papers. IR 466, 2006.
[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.
[16]
S. Teufel, A. Siddharthan, and D. Tidhar. Automatic classification of citation function. In Proceedings of EMNLP-06, 2006.
[17]
B. Webb. Netflix update: Try this at home. In http://sifter.org/$\sim$simon/journal/20061211.html, 2006.

Cited By

View all
  • (2024)Paper Recommender System Using Big Data ToolsOptimization Algorithms - Classics and Recent Advances10.5772/intechopen.109136Online publication date: 10-Jul-2024
  • (2024)Sentiment Dimensions and Intentions in Scientific Analysis: Multilevel Classification in Text and CitationsElectronics10.3390/electronics1309175313:9(1753)Online publication date: 2-May-2024
  • (2024)Human-Computer Interaction in the Age of Generative AI: Tailoring Educational Content for Diverse LearnersEmerging Technologies for Education10.1007/978-981-97-4246-2_12(137-146)Online publication date: 16-Jul-2024
  • Show More Cited By

Index Terms

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

      Recommendations

      Comments

      Information & Contributors

      Information

      Published In

      cover image ACM Conferences
      JCDL '13: Proceedings of the 13th ACM/IEEE-CS joint conference on Digital libraries
      July 2013
      480 pages
      ISBN:9781450320771
      DOI:10.1145/2467696
      Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

      Sponsors

      Publisher

      Association for Computing Machinery

      New York, NY, United States

      Publication History

      Published: 22 July 2013

      Permissions

      Request permissions for this article.

      Check for updates

      Author Tags

      1. citation recommendation
      2. collaborative filtering
      3. information filtering
      4. singular value decomposition

      Qualifiers

      • Research-article

      Conference

      JCDL '13
      Sponsor:
      JCDL '13: 13th ACM/IEEE-CS Joint Conference on Digital Libraries
      July 22 - 26, 2013
      Indiana, Indianapolis, USA

      Acceptance Rates

      JCDL '13 Paper Acceptance Rate 28 of 95 submissions, 29%;
      Overall Acceptance Rate 415 of 1,482 submissions, 28%

      Contributors

      Other Metrics

      Bibliometrics & Citations

      Bibliometrics

      Article Metrics

      • Downloads (Last 12 months)18
      • Downloads (Last 6 weeks)1
      Reflects downloads up to 11 Jan 2025

      Other Metrics

      Citations

      Cited By

      View all
      • (2024)Paper Recommender System Using Big Data ToolsOptimization Algorithms - Classics and Recent Advances10.5772/intechopen.109136Online publication date: 10-Jul-2024
      • (2024)Sentiment Dimensions and Intentions in Scientific Analysis: Multilevel Classification in Text and CitationsElectronics10.3390/electronics1309175313:9(1753)Online publication date: 2-May-2024
      • (2024)Human-Computer Interaction in the Age of Generative AI: Tailoring Educational Content for Diverse LearnersEmerging Technologies for Education10.1007/978-981-97-4246-2_12(137-146)Online publication date: 16-Jul-2024
      • (2024)Empowering Legal Citation Recommendation via Efficient Instruction-Tuning of Pre-trained Language ModelsAdvances in Information Retrieval10.1007/978-3-031-56027-9_19(310-324)Online publication date: 20-Mar-2024
      • (2023)A Systematic Review of Citation Recommendation Over the Past Two DecadesInternational Journal on Semantic Web and Information Systems10.4018/IJSWIS.32407119:1(1-22)Online publication date: 1-Jun-2023
      • (2023)An anatomization of research paper recommender systemEngineering Applications of Artificial Intelligence10.1016/j.engappai.2022.105641118:COnline publication date: 1-Feb-2023
      • (2022)MP-BERT4REC: Recommending Multiple Positive Citations for Academic Manuscripts via Content-Dependent BERT and Multi-Positive TripletIEICE Transactions on Information and Systems10.1587/transinf.2022EDP7034E105.D:11(1957-1968)Online publication date: 1-Nov-2022
      • (2022)Dual Attention Model for Citation Recommendation with Analyses on Explainability of Attention Mechanisms and Qualitative ExperimentsComputational Linguistics10.1162/coli_a_0043848:2(403-470)Online publication date: 9-Jun-2022
      • (2022)Countering Disinformation by Finding Reliable Sources: a Citation-Based Approach2022 International Joint Conference on Neural Networks (IJCNN)10.1109/IJCNN55064.2022.9891941(1-8)Online publication date: 18-Jul-2022
      • (2022)Enhancing citation recommendation using citation network embeddingScientometrics10.1007/s11192-021-04196-3Online publication date: 11-Jan-2022
      • Show More Cited By

      View Options

      Login options

      View options

      PDF

      View or Download as a PDF file.

      PDF

      eReader

      View online with eReader.

      eReader

      Media

      Figures

      Other

      Tables

      Share

      Share

      Share this Publication link

      Share on social media