Tamara Heck, Isabella Peters, Wolfgang G. Stock Dept. of Information Science Heinrich-Heine-University Düsseldorf     Test...
Research Questions   Aim: Recommend relevant partners for target    scientist                      More                  ...
Methods I+II   Author Co-Citation in Scopus:       ACC:= (D, Ca, Q) where Q ⊆ D x Ca with |Q| > 0            where Ca i...
Method III   Collaborative Filtering in CiteULike:       Folksonomy F: = (U, T, R, Y) with Y ⊆U x T x R            Docs...
Method III             collaborative filtering for author recommendation
Results & Evaluation I   4 Clusters with at least 30 similar authors       COCI: author co-citation in Scopus       BIC...
Results & Evaluation I   Important authors found:                                                              67        ...
Results & Evaluation I   Coverage of important authors in the    recommendation of the Top 20 authors:        100%       ...
Results & Evaluation II   4 graphs:       cosine values between all authors of one cluster       Evaluation graph analy...
Results & Evaluation II       CCULT graph: 7        cosine interval: 0.49-0.99                collaborative filtering for ...
Results & Evaluation II   Relevance:       COCI: 5.08       BICO: 8.7       CULU: 2.13       CULT:.5.25 Graph helpfu...
Further work   Insights:       CUL data complements COCI and BICO       Need for expert recommendation       Graph arr...
Limitations & problems   Datasets:       CiteULike: Sparse data, misspelled author        names, tags not consistent    ...
ReferencesAhlgren,P., Jarneving, B. and Rousseau, R. 2003. Requirements for a cocitation similarity measure, with special...
ReferencesLee,   D. H. and Brusilovky, P. 2010a. Social networks and interest similarity. The case of CiteULike. In Proce...
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Testing Collaborative Filtering against Co-Citation Analysis and Bibliographic Coupling for Academic Author Recommendation

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Presentation at 3rd Workshop on Recommender Systems and the Social Web on ACM RecSys’11 on 23rd October in Chicago, IL, USA 

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Testing Collaborative Filtering against Co-Citation Analysis and Bibliographic Coupling for Academic Author Recommendation

  1. 1. Tamara Heck, Isabella Peters, Wolfgang G. Stock Dept. of Information Science Heinrich-Heine-University Düsseldorf Testing Collaborative Filtering againstCo-Citation Analysis and Bibliographic Coupling for Academic Author Recommendation 3rd Workshop on Recommender Systems and the Social Web on ACM RecSys’11 on 23rd October in Chicago, IL, USA
  2. 2. Research Questions Aim: Recommend relevant partners for target scientist More like me!  for co-authorship  establishment of a community of practice  search for contributions to a handbook Can we propose a network with relevant collaboration partners to a target researcher with collaborative filtering in CiteULike? Are these results different to co-citation analysis and bibliographic coupling? collaborative filtering for author recommendation
  3. 3. Methods I+II Author Co-Citation in Scopus:  ACC:= (D, Ca, Q) where Q ⊆ D x Ca with |Q| > 0  where Ca is the set of cited articles of target author a. Bibliographic Coupling in Web of Science:  BC:= (Refd(a), D, S) where S ⊆ Refd(a) x D and {d ∈ D | Refd(a)| ≥ n, n ℕ}  where Refd(a) is the number of references in one document d of target author a.  “related records”: number of common references in a single document important collaborative filtering for author recommendation
  4. 4. Method III Collaborative Filtering in CiteULike:  Folksonomy F: = (U, T, R, Y) with Y ⊆U x T x R  Docsonomy DF:= (T, R, Z)  Personomy PUT:= (U, T, X)  Personal bookmark list: PBLUR:= (U, R, W)  2 opportunities:  1. All users u U who have at least one article of the target author a in their bookmark list: PBLURa:= (U, Ra, W) where W ⊆ U x Ra  2. All documents to which users assigned the same tags like to the target author’s a articles: DFa:= (Ta, R, Z) where Z ⊆ Ta xR collaborative filtering for author recommendation
  5. 5. Method III collaborative filtering for author recommendation
  6. 6. Results & Evaluation I 4 Clusters with at least 30 similar authors  COCI: author co-citation in Scopus  BICO: common references in WoS  CULU: CV based on common users in CiteULike  CULT: CV based on common tags in CiteULike Evaluation:  10 top ranked authors of each cluster  identify known authors/partners and research field  identify relevance for own research: rating 1 (not important) till 10 (very important)  tell relevant authors not on the list collaborative filtering for author recommendation
  7. 7. Results & Evaluation I Important authors found: 67 64 27 Web of Scopus Science 12 24 16 70 CiteULike collaborative filtering for author recommendation
  8. 8. Results & Evaluation I Coverage of important authors in the recommendation of the Top 20 authors: 100% 90% 80% COCI 70% BICO 60% CULU 50% 40% CULT 30% 20% 10% 0% author 1 author 2 author 3 author 4 author 5 author 6 collaborative filtering for author recommendation
  9. 9. Results & Evaluation II 4 graphs:  cosine values between all authors of one cluster  Evaluation graph analysis:  Is the distribution of the authors/ author communities correspondent to the communities in reality?  Where do your see yourself in the community?  Would this graph be helpful e.g. to start a project or organize a workshop or scientific conference?  How relevant is the graph: rating 1 till 10? collaborative filtering for author recommendation
  10. 10. Results & Evaluation II CCULT graph: 7 cosine interval: 0.49-0.99 collaborative filtering for author recommendation
  11. 11. Results & Evaluation II Relevance:  COCI: 5.08  BICO: 8.7  CULU: 2.13  CULT:.5.25 Graph helpful to find new unknown collaboration partners CULU e. CULT show more unknown authors COCI e. BICO show many relevant known authors collaborative filtering for author recommendation
  12. 12. Further work Insights:  CUL data complements COCI and BICO  Need for expert recommendation  Graph arrangement must be clear Questions:  How to combine methods?  How to visualize graphs?  Which algorithms to use? collaborative filtering for author recommendation
  13. 13. Limitations & problems Datasets:  CiteULike: Sparse data, misspelled author names, tags not consistent  Scopus: discrepancies with co-authors  Data not complete:  5 of 14 authors have complete coverage  3 have coverage between 70 % and 90 %  5 between 55 % and 70 %  1 author only a coverage of 33 %  WoS: author identification difficult  Author articles to be generated manually collaborative filtering for author recommendation
  14. 14. ReferencesAhlgren,P., Jarneving, B. and Rousseau, R. 2003. Requirements for a cocitation similarity measure, with special reference to Pearson’s correlation coefficient. Journal of the American Society for Information Science andTechnology, 54(6), 550-560.Ahn, H. J. 2008. A new similarity measure for collaborative filtering to alleviate the new user cold-starting problem. Information Sciences, 178, 37-51.Au Yeung, C. M., Noll, M., Gibbins, N., Meinel, C. and Shadbolt, N. 2009. On measuring expertise in collaborative tagging systems. Web Science Conference: Society On-Line, 18th-20th March 2009, Athens, Greece.Ben Jabeur, L., Tamine, L. and Boughanem, M. 2010. A social model for literature access: towards a weighted social network of authors. Proceedings of RIAO 10 International Conference on Adaptivity, Personalizationand Fusion of Heterogeneous Information. Paris, France, 32-39.Berkovsky, S., Kuflik, T. and Ricci F. 2007. Mediation of user models for enhanced personalization in recommender systems. User Model User-Adap Inter, 18, 245-286.Bichteler, J. and Eaton, E. A. 1980. The combined use of bibliographic coupling and cocitation for document-retrieval. Journal of the American Society for Information Science, 31(4), 278-282.Blazek, R. 2007. Author-Statement Citation Analysis Applied as a Recommender System to Support Non-Domain-Expert Academic Research. Doctoral Dissertation. Fort Lauderdale, FL: Nova Southeastern University.Bogers, T. and van den Bosch, A. 2008. Recommending scientific articles using CiteULike. Proceedings of the 2008 ACM Conference on Recommender Systems. New York, NY, 287-290.Boyack, K. W. and Klavans, R. 2010. Co-citation analysis, bibliographic coupling, and direct citation. Which citation approach represents the research front most accurately? Journal of the American Society forInformation Science and Technology, 61(12), 2389-2404.Cabanac, G. 2010. Accuracy of inter-researcher similarity measures based on topical and social clues. Scientometrics, 87(3), 597-620.Cacheda,F., Carneiro, V., Fernández, D. and Formoso, V. 2011. Comparison of collaborative filtering algorithms: Limitations of current techniques and proposals for scalable, high-performance recommender systems.ACM Transactions on the Web, 5(1), article 2.Cai,X., Bain, M., Krzywicki, A., Wobcke, W., Kim, Y. S., Compton, P. and Mahidadia, A. 2011. Collaborative filtering for people to people recommendation in social networks. Lecture Notes in ComputerScience, 6464, 476-485.Cawkell, T. 2000. Methods of information retrieval using Web of Science. Pulmonary hypertension as a subject example. Journal of Information Science, 26(1), 66-70.Cronin, B. 1984. The Citation Process. The Role and Significance of Citations in Scientific Communication. London, UK: Taylor Graham.Cruz, C. C. P., Motta, C. L. R., Santoro, F. M. and Elia, M. 2009. Applying reputation mechanisms in communities of practice. A case study. Journal of Universal Computer Science, 15(9), 1886-1906.Desrosiers, C. and Karypis, G. 2011. A comprehensive survey of neighborhood-based recommendation methods (pp. 197-144). In Ricci, F., Rokach, L., Shapira, B. and Kantor, P.B (Eds.), Recommender SystemsHandbook. Springer, NY.Egghe, L. 2010. Good properties of similarity measures and their complementarity. Journal of the American Society for Information Science and Technology, 61(10), 2151-2160.Gmur, M. 2003. Co-citation analysis and the search for invisible colleges. A methodological evaluation. Scientometrics, 57(1), 27-57.Hamers, L., Hemeryck, Y., Herweyers, G. and Janssen, M. 1989. Similarity measures in scientometric research: The Jaccard Index versus Salton’s cosine formula. Information Processing & Management, 25(3), 315-318.Haustein, S. and Siebenlist, T. 2011. Applying social bookmarking data to evaluate journal usage. Journal of Informetrics, 5, 446-457.Heck, T. (2011). A comparison of different user-similarity measures as basis for research and scientific cooperation. Information Science and Social Media International Conference August 24-26, Åbo/Turku, Finland.Heck,T. and Peters, I. 2010. Expert recommender systems: Establishing Communities of Practice based on social bookmarking systems. In Proceedings of I-Know 2010,10th International Conference on KnowledgeManagement and Knowledge Technologies, 458-464. J. L., Konstan, J. A., Borchers, A. and Riedl, J. 1999. An algorithmic framework for performing collaborative filtering. Proceedings of the 22nd Annual International ACM SIGIR Conference on Research andHerlocker,Development in Information Retrieval. New York, 230-237.Herlocker, J. L., Konstan, J. A., Terveen L. G. and Riedl, J. T. 2004. Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems, 22(1), 5-53.Hotho, A., Jäschke, R., Schmitz, C. and Stumme, G. 2006. Information retrieval in folksonomies: Search and ranking (pp. 411-426). In Sure, Y., Domingue, J. (Eds.), The Semantic Web: Research andApplications, Lecture Notes in Computer Science 4011, Springer, Heidelberg.Kessler, M. M. 1963. Bibliographic coupling between scientific papers. American Documentation, 14, 10-25.Krohn-Grimberghe, A., Nanopoulos, A. and Schmidt-Thieme, L. 2010. A novel multidimensional framework for evaluating recommender systems. In Proceedings of the ACM RecSys 2010 Workshop on User-CentricEvaluation of Recommender Systems and Their Interfaces (UCERSTI). New York, NY, ACM. collaborative filtering for author recommendation
  15. 15. ReferencesLee, D. H. and Brusilovky, P. 2010a. Social networks and interest similarity. The case of CiteULike. In Proceedings of the 21st ACM Conference on Hypertext & Hypermedia, Toronto, Canada, 151-155.Lee, D. H. and Brusilovky, P. 2010b. Using self-defined group activities for improving recommendations in collaborative tagging systems. In Proceedings of the Fourth ACM Conference on Recommender Systems.NY, 221-224.Leydesdorff, L. 2005. Similarity measures, author cocitation analysis, and information theory. Journal of the American Society for Information Science and Technology, 56(7), 769-772.Leydesdorff, L. 2008. On the normalization and visualization of author co-citation data. Salton’s cosine versus the Jaccard index. Journal of the American Society for Information Science and Technology, 59(1), 77-85.Li, J., Burnham, J. F., Lemley, T. and Britton, R. M. 2010. Citation analysis. Comparison of Web of Science, Scopus, SciFinder, and Google Scholar. Journal of Electronic Resources in Medical Libraries, 7(3), 196-217.Liang, H., Xu, Y., Li, Y. and Nayak, R. 2008. Collaborative filtering recommender systems using tag information. ACM International Conference on Web Intelligence and Intelligent Agent Technology. New York, NY, 59-62.Linde, F. and Stock, W.G. 2011. Information Markets. Berlin, Germany, New York, NY: De Gruyter Saur.Luo, H., Niu, C., Shen, R. and Ullrich, C. 2008. A collaborative filtering framework based on both local user similarity and global user similarity. Machine Learning, 72(3), 231-245.Marinho, L. B., Nanopoulos, A., Schmidt-Thieme, L., Jäschke, R., Hotho, A., Stumme, G. and Symeonidis, P. 2011. Social tagging recommenders systems (pp. 615-644). In Ricci, F., Rokach, L., Shapira, B. andKantor, P.B (Eds.), Recommender Systems Handbook. Springer, NY.McNee, S. M., Kapoor, N. and Konstan, J.A. 2006. Don’t look stupid. Avoiding pitfalls when recommending research papers. In Proc. of the 20th anniversary Conference on Computer Supported Cooperative Work. NewYork, NY, ACM, 171-180.Meho, L. I. and Rogers, Y. 2008. Citation counting, citation ranking, and h-index of human-computer interaction researchers. A comparison of Scopus and Web of Science. Journal of the American Society for InformationScience and Technology, 59(11), 1711-1726.Meho, L. I. and Sugimoto, C. R. 2009. Assessing the scholarly impact of information studies. A tale of two citation databases – Scopus and Web of Science. Journal of the American Society for Information Science andTechnology, 60(12), 2499-2508.Parra, D. and Brusilovsky, P. 2009. Collaborative filtering for social tagging systems. An Experiment with CiteULike. In Proc. of the Third ACM Conference on Recommender Systems. New York, NY, ACM, 237-240.Peters, I. 2009. Folksonomies. Indexing and Retrieval in Web 2.0. Berlin, Germany: De Gruyter Saur.Ramezani, M., Bergman, L., Thompson, R., Burke, R. and Mobasher, B. 2008. Selecting and applying recommendation technology. In Proc. of International Workshop on Recommendation and Collaboration, inConjunction with 2008 International ACM Conference on Intelligent User Interfaces. Canaria, Canary Islands, Spain.Petry, H., Tedesco, P., Vieira, V. and Salgado, A. C. 2008. ICARE. A context-sensitive expert recommendation system. In The 18th European Conference on Artificial Intelligence. Workshop on Recommender Systems.Patras, Greece, 53-58.Rendle,S., Marinho, L. B., Nanopoulos, A. and Schmidt-Thieme, L. 2009. Learning optimal ranking with tensor factorization for tag recommendation. In Proceedings of the 15th ACM SIGKDD International Conference onKnowledge Discovery and Data Mining. New York, NY, 727-736.Resnick,P., Iacovou, N., Suchak, M., Bergstrom, P. and Riedl, J. 1994. Grouplens: An open architecture for collaborative filtering of netnews. In Proc. of CSCW’94, ACM Conference on Computer Supported CooperativeWork. New York, NY, ACM, 175-186.Schneider, J.W. and Borlund, P. 2007a. Matrix Comparison, Part 1: Motivation and important issues for measuring the resemblance between proximity measures or ordination results. Journal of the American Society forInformation Science and Technology, 58(11), 1586-1595.Schneider, J. W. and Borlund, P. 2007b. Matrix Comparison, Part 2: Measuring the resemblance between proximity measures or ordination results by use of the Mantel and Procrustes statistics. Journal of the AmericanSociety for Information Science and Technology, 58(11), 1596-1609.Shepitsen,A., Gemmell, J., Mobasher, B. and Burke, R. 2008. Personalized recommendation in social tagging systems using hierarchical clustering. In Proc. of the 2008 ACM Conference on Recommender Systems.NY, 259-266.Small, H. 1973. Cocitation in scientific literature. New measure of relationship between 2 documents. Journal of the American Society for Information Science, 24(4), 265-269.Stock, W. G. 1999. Web of Science. Ein Netz wissenschaftlicher Informationen – gesponnen aus Fußnoten [Web of Science. A web of scientific information – cocooned from footnotes]. Password, no. 7+8, 21-25.Zanardi, V. and Capra, L. 2008. Social ranking: Uncovering relevant content using tag-based recommender systems. Proceedings of the 2008 ACM Conference on Recommender Systems. New York, NY, 51-58.Zhao, D. and Strotmann, A. 2011. Counting first, last, or all authors in citation analysis. Collaborative stem cell research field. Journal of the American Society for Information Science and Technology, 62(4), 654-67. collaborative filtering for author recommendation

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