Abstract
Online tools for collaboration and social platforms have become omnipresent in Web-based environments. Interests and skills of people evolve over time depending in performed activities and joint collaborations. We believe that ranking models for recommending experts or collaboration partners should not only rely on profiles or skill information that need to be manually maintained and updated by the user. In this work we address the problem of expertise mining based on performed interactions between people. We argue that an expertise mining algorithm must consider a person’s interest and activity level in a certain collaboration context. Our approach is based on the PageRank algorithm enhanced by techniques to incorporate contextual link information. An approach comprising two steps is presented. First, offline analysis of human interactions considering tagged interaction links and second composition of ranking scores based on preferences. We evaluate our approach using an email interaction network.
This work is supported by the EU FP7 project COIN (No. ICT-2008-216256).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Schall, D., Truong, H.L., Dustdar, S.: Unifying Human and Software Services in Web-Scale Collaborations. IEEE Internet Computing 12(3), 62–68 (2008)
Becerra-Fernandez, I.: Searching for experts on the Web: A review of contemporary expertise locator systems. ACM Trans. Inter. Tech. 6(4), 333–355 (2006)
Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank Citation Ranking: Bringing Order to the Web. Technical report (1998)
Kleinberg, J.M.: Authoritative sources in a hyperlinked environment. J. ACM 46(5), 604–632 (1999)
Zhang, J., Ackerman, M.S., Adamic, L.: Expertise networks in online communities: structure and algorithms. In: WWW, pp. 221–230. ACM, New York (2007)
Jurczyk, P., Agichtein, E.: Discovering authorities in question answer communities by using link analysis. In: CIKM 2007, pp. 919–922. ACM, New York (2007)
Berkhin, P.: A survey on pagerank computing. Internet Mathematics 2, 73–120 (2005)
Schall, D., Skopik, F., Dustdar, S.: Trust-based discovery and interactions in mixed service-oriented systems. Technical Report TUV-1841-2010-01 (2010)
Skopik, F., Schall, D., Dustdar, S.: Start trusting strangers? bootstrapping and prediction of trust. In: Vossen, G., Long, D.D.E., Yu, J.X. (eds.) WISE 2009. LNCS, vol. 5802, pp. 275–289. Springer, Heidelberg (2009)
Haveliwala, T.H.: Topic-sensitive pagerank. In: WWW, pp. 517–526 (2002)
Jeh, G., Widom, J.: Scaling personalized web search. In: WWW, pp. 271–279. ACM, New York (2003)
Schall, D.: Human Interactions in Mixed Systems - Architecture, Protocols, and Algorithms. PhD thesis, Vienna University of Technology (2009)
Fogaras, D., Csalogany, K., Racz, B., Sarlos, T.: Towards scaling fully personalized pagerank: Algorithms, lower bounds, and experiments. Internet Mathematics 2(3), 333–358 (2005)
Maslov, S., Redner, S.: Promise and pitfalls of extending google’s pagerank algorithm to citation networks. J. Neurosci. 28(44), 11103–11105 (2008)
Yang, J., Adamic, L., Ackerman, M.: Competing to share expertise: the taskcn knowledge sharing community. In: Conference on Weblogs and Social Media (2008)
Agichtein, E., Castillo, C., Donato, D., Gionis, A., Mishne, G.: Finding high-quality content in social media. In: WSDM 2008, pp. 183–194. ACM, New York (2008)
Dom, B., Eiron, I., Cozzi, A., Zhang, Y.: Graph-based ranking algorithms for e-mail expertise analysis. In: DMKD 2003, pp. 42–48. ACM, New York (2003)
Shetty, J., Adibi, J.: Discovering important nodes through graph entropy the case of enron email database. In: LinkKDD 2005, pp. 74–81. ACM, New York (2005)
Karagiannis, T., Vojnovic, M.: Email Information Flow in Large-Scale Enterprises. Technical report, Microsoft Research (2008)
Lappas, T., Liu, K., Terzi, E.: Finding a team of experts in social networks. In: KDD 2009, pp. 467–476. ACM, New York (2009)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Schall, D., Dustdar, S. (2010). Dynamic Context-Sensitive PageRank for Expertise Mining. In: Bolc, L., Makowski, M., Wierzbicki, A. (eds) Social Informatics. SocInfo 2010. Lecture Notes in Computer Science, vol 6430. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16567-2_12
Download citation
DOI: https://doi.org/10.1007/978-3-642-16567-2_12
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-16566-5
Online ISBN: 978-3-642-16567-2
eBook Packages: Computer ScienceComputer Science (R0)