Skip to main content

Collaboration Recommendation on Academic Social Networks

  • Conference paper
Book cover Advances in Conceptual Modeling – Applications and Challenges (ER 2010)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 6413))

Included in the following conference series:

Abstract

In the academic context, scientific research works are often performed through collaboration and cooperation between researchers and research groups. Researchers work in various subjects and in several research areas. Identifying new partners to execute joint research and analyzing the level of cooperation of the current partners can be very complex tasks. Recommendation of new collaborations may be a valuable tool for reinforcing and discovering such partners. This paper presents an innovative approach to recommend collaborations on the context of academic Social Networks. Specifically, we introduce the architecture for such approach and the metrics involved in recommending collaborations. We also present an initial case study to validate our approach.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Smeaton, A.F., Callan, J.: Personalisation and recommender systems in digital libraries. Int. J. on Digital Libraries 5, 299–308 (2005)

    Article  Google Scholar 

  2. Perugini, S., Gonçalves, M.A., Fox, E.A.: Recommender systems research: A connection-centric survey. J. Intell. Inf. Syst. 23, 107–143 (2004)

    Article  MATH  Google Scholar 

  3. Loh, S., et al.: Constructing domain ontologies for indexing texts and creating users’ profiles. In: Work. on Ontologies and Metamodeling in Software and Data Engineering, Brazilian Symp. on Databases, UFSC, Florianópolis, pp. 72–82 (2006)

    Google Scholar 

  4. Aleman-Meza, B., et al.: Semantic analytics on social networks: experiences in addressing the problem of conflict of interest detection. In: Intl. Conf. on World Wide Web, pp. 407–416. ACM Press, New York (2006)

    Google Scholar 

  5. Mika, P.: Social networks and the semantic web. In: IEEE/WIC/ACM Intl. Conf. on Web Intelligence, pp. 285–291. IEEE Press, New York (2004)

    Chapter  Google Scholar 

  6. Salton, G., Buckley, C.: Term-weighting approaches in automatic text retrieval. Inf. Process. Manage. 24, 513–523 (1988)

    Article  Google Scholar 

  7. Ogata, H., Yano, Y., Furugori, N., Jin, Q.: Computer supported social networking for augmenting cooperation. Comput. Supp. Coop. Work 10, 189–209 (2001)

    Article  Google Scholar 

  8. Golbeck, J., Hendler, J.: Filmtrust: movie recommendations using trust in web-based social networks. In: Consumer Communications and Networking Conf., pp. 282–286. IEEE Press, New York (2006)

    Google Scholar 

  9. Quercia, D., Capra, L.: Friendsensing: recommending friends using mobile phones. In: ACM Conference on Recommender Systems, pp. 273–276. ACM Press, New York (2009)

    Google Scholar 

  10. Karagiannis, T., Vojnovic, M.: Behavioral profiles for advanced email features. In: Intl. Conf. on World Wide Web, pp. 711–720. ACM Press, New York (2009)

    Google Scholar 

  11. Chen, J., et al.: Make new friends, but keep the old: recommending people on social networking sites. In: SIGCHI Conference on Human Factors in Computing Systems, pp. 201–210. ACM Press, New York (2009)

    Google Scholar 

  12. Kautz, H., Selman, B., Shah, M.: Referral web: combining social networks and collaborative filtering. Commun. ACM 40, 63–65 (1997)

    Article  Google Scholar 

  13. McDonald, D.W.: Recommending collaboration with social networks: a comparative evaluation. In: SIGCHI Conference on Human Factors in Computing Systems, pp. 593–600. ACM Press, New York (2003)

    Google Scholar 

  14. Zaiane, O.R., Chen, J., Goebel, R.: Dbconnect: mining research community on dblp data. In: WebKDD and SNA-KDD Work. on Web Mining and Social Network Analysis, pp. 74–81. ACM Press, New York (2007)

    Google Scholar 

  15. Weng, S.S., Chang, H.L.: Using ontology network analysis for research document recommendation. Expert Syst. Appl. 34, 1857–1869 (2008)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2010 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Lopes, G.R., Moro, M.M., Wives, L.K., de Oliveira, J.P.M. (2010). Collaboration Recommendation on Academic Social Networks. In: Trujillo, J., et al. Advances in Conceptual Modeling – Applications and Challenges. ER 2010. Lecture Notes in Computer Science, vol 6413. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-16385-2_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-16385-2_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-16384-5

  • Online ISBN: 978-3-642-16385-2

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics