You Never Walk Alone: Recommending Academic Events Based on Social Network Analysis

  • Ralf Klamma
  • Pham Manh Cuong
  • Yiwei Cao
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 4)


Combining Social Network Analysis and recommender systems is a challenging research field. In scientific communities, recommender systems have been applied to provide useful tools for papers, books as well as expert finding. However, academic events (conferences, workshops, international symposiums etc.) are an important driven forces to move forwards cooperation among research communities. We realize a SNA based approach for academic events recommendation problem. Scientific communities analysis and visualization are performed to provide an insight into the communities of event series. A prototype is implemented based on the data from DBLP and, and the result is observed in order to prove the approach.


Recommender systems Social Network Analysis community analysis community of practice information visualization 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Schafer, J.B., Konstan, J.A., Riedl, J.: E-commerce recommendation applications. Data Min. Knowl. Discov. 5(1-2), 115–153 (2001)CrossRefzbMATHGoogle Scholar
  2. 2.
    Burke, R.: Hybrid recommender systems: Survey and experiments. User Modeling and User-Adapted Interaction 12(4), 331–370 (2002)CrossRefzbMATHGoogle Scholar
  3. 3.
    Sarwar, B., Karypis, G., Konstan, J., Reidl, J.: Item-based collaborative filtering recommendation algorithms. In: Proceedings of the 10th international conference on World Wide Web, pp. 285–295. ACM Press, New York (2001)Google Scholar
  4. 4.
    Breese, J.S., Heckerman, D., Kadie, C.M.: Empirical analysis of predictive algorithms for collaborative filtering, pp. 43–52 (1998)Google Scholar
  5. 5.
    Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: A survey of the stateof-the-art and possible extensions. IEEE Transactions on Knowledge and Data Engineering 17(6), 734–749 (2005)CrossRefGoogle Scholar
  6. 6.
    Zhuang, Z., Elmacioglu, E., Lee, D., Giles, C.L.: Measuring conference quality by mining program committee characteristics. In: Proceedings of the 2007 conference on Digital libraries, pp. 225–234. ACM Press, New York (2007)CrossRefGoogle Scholar
  7. 7.
    Latour, B.: On recalling ant. In: Law, J., Hassard, J. (eds.) Actor-Network Theory and After, pp. 15–25 (1999)Google Scholar
  8. 8.
    Denev, D.: Multidimensional Patterns of Disturbance in Digital Social Networks. Master’s thesis, RWTH Aachen University (2006)Google Scholar
  9. 9.
    Couldry, N.: Actor Network Theory and Media: Do They Connect and On What Terms? In: Hepp, A., et al. (eds.) Cultures of Connectivity. School of Economics and Political Science, London, pp. 1–14 (2004)Google Scholar
  10. 10.
    Newman, M.E.: Scientific collaboration networks. i. network construction and fundamental results. Phys. Rev. E. Stat. Nonlin. Soft. Matter. Phys. 64(1-2) (2001)Google Scholar
  11. 11.
    Newman, M.E.: Coauthorship networks and patterns of scientific collaboration. Proc. Natl. Acad. Sci. USA 101, 5200–5205 (2004)CrossRefGoogle Scholar
  12. 12.
    Huang, T.H., Huang, M.L.: Analysis and visualization of co-authorship networks for understanding academic collaboration and knowledge domain of individual researchers. In: CGIV 2006: Proceedings of the International Conference on Computer Graphics, Imaging and Visualisation, pp. 18–23. IEEE Computer Society, Washington (2006)Google Scholar
  13. 13.
    Kienle, A., Wesser, M.: Principles for cultivating scientific communities of practice. In: Proceedings of the 2nd International Conference on Communities and Technologies, pp. 283–299. Springer Netherlands (2005)Google Scholar
  14. 14.
    Wenger, E., McDermott, R., Snyder, W.M.: Cutivating Communities of Practice: A guid to Managing Knowledge. Havard Business School Press, Campridge (2002)Google Scholar
  15. 15.
    Yan, S., Lee, D.: Toward alternative measures for ranking venues: a case of database research community. In: Proceedings of the 2007 conference on Digital libraries, pp. 235–244. ACM Press, New York (2007)CrossRefGoogle Scholar
  16. 16.
    Rahm, E., Thor, A.: Citation analysis of database publications. SIGMOD Record 34(4), 48–53 (2005)CrossRefGoogle Scholar
  17. 17.
    Sidiropoulos, A., Manolopoulos, Y.: A citation-based system to assist prize awarding. SIGMOD Record 34(4), 54–60 (2005)CrossRefGoogle Scholar
  18. 18.
    Sidiropoulos, A., Manolopoulos, Y.: A new perspective to automatically rank scientific conferences using digital libraries. Inf. Process. Manage. 41(2), 289–312 (2005)CrossRefGoogle Scholar
  19. 19.
    McNee, S.M., Albert, I., Cosley, D., Gopalkrishnan, P., Lam, S.K., Rashid, A.M., Konstan, J.A., Riedl, J.: On the recommending of citations for research papers. In: Proceedings of the 2002 ACM conference on Computer supported cooperative work, pp. 116–125. ACM Press, New York (2002)CrossRefGoogle Scholar
  20. 20.
    Mooney, R.J., Roy, L.: Content-based book recommending using learning for text categorization. In: Proceedings of the Fifth ACM Conference on Digital Libraries, pp. 195–204. ACM Press, New York (2000)CrossRefGoogle Scholar
  21. 21.
    Torres, R., McNee, S.M., Abel, M., Konstan, J.A., Riedl, J.: Enhancing digital libraries with TechLens+. In: Proceedings of the 4th ACM/IEEE-CS Joint Conference on Digital Libraries, pp. 228–236. ACM Press, New York (2004)Google Scholar

Copyright information

© ICST Institute for Computer Science, Social Informatics and Telecommunications Engineering 2009

Authors and Affiliations

  • Ralf Klamma
    • 1
  • Pham Manh Cuong
    • 1
  • Yiwei Cao
    • 1
  1. 1.Databases & Information SystemsRWTH Aachen UniversityAachenGermany

Personalised recommendations