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Scientific Recommendations to Enhance Scholarly Awareness and Foster Collaboration

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Book cover Recommender Systems for Technology Enhanced Learning

Abstract

Recommender systems have become an essential part of the web user’s life. Whether it is recommended books or movies, friends on social networks or mobile phone contracts, service providers have realized that personalized recommendations and ads increase customer retention and satisfaction. Last but not lease, recommender systems can help selling more goods. Scientific recommender systems, on the other hand, have the goal to recommend useful scholarly objects such as publications, conferences or researchers to the interested researcher in order to make them aware of them and to foster collaboration and scientific exchange. In this paper we introduce PUSHPIN, a social network for researchers and its recommender approach. PUSHPIN is based on an eResearch infrastructure that analyzes large corpora of scientific publications and combines the extracted data with the social interactions in an active social network.

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Notes

  1. 1.

    PUSHPIN is available as a free service at http://pushpin.cs.upb.de.

  2. 2.

    For example, http://amazon.com.

  3. 3.

    Such as http://netflix.com, http://last.fm or http://spotify.com.

  4. 4.

    http://www.scienstein.org/.

  5. 5.

    http://sciplore.org/.

  6. 6.

    http://www.docear.org/.

  7. 7.

    http://citeseerx.ist.psu.edu.

  8. 8.

    http://scholar.google.com.

  9. 9.

    http://www.mendeley.com/.

  10. 10.

    https://www.researchgate.net/.

  11. 11.

    http://hadoop.apache.org.

  12. 12.

    http://storm-project.net/.

  13. 13.

    http://mahout.apache.org.

  14. 14.

    http://hbase.apache.org.

  15. 15.

    http://activemq.apache.org.

  16. 16.

    http://wing.comp.nus.edu.sg/parsCit/.

  17. 17.

    http://grobid.no-ip.org.

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Acknowledgements

The research presented in this article was financially supported by a funding from the University Paderborn’s Commission for Research and Young Scientists. We also thank the other members of the PUSHPIN project for their efforts.

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Authors and Affiliations

  1. Department of Computer Science, University of Paderborn, Fürstenallee 11, 33102, Paderborn, Germany

    Jan Petertonkoker, Wolfgang Reinhardt, Junaid Surve & Pragati Sureka

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  1. Jan Petertonkoker
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  2. Wolfgang Reinhardt
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  3. Junaid Surve
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  4. Pragati Sureka
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Correspondence to Wolfgang Reinhardt .

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Editors and Affiliations

  1. Agro-Know, Athens, Greece

    Nikos Manouselis

  2. Faculty of Psychology and Educational Sciences, Welten Institute – Research Centre for Learning, Teaching and Technology, Open University of the Netherlands, Heerlen, The Netherlands

    Hendrik Drachsler

  3. Department of Computer Science, VUB & KU Leuven, Leuven, Belgium

    Katrien Verbert

  4. aDeNu Research Group, Artificial Intelligence Department, Computer Science School, UNED, Madrid, Spain

    Olga C. Santos

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Petertonkoker, J., Reinhardt, W., Surve, J., Sureka, P. (2014). Scientific Recommendations to Enhance Scholarly Awareness and Foster Collaboration. In: Manouselis, N., Drachsler, H., Verbert, K., Santos, O. (eds) Recommender Systems for Technology Enhanced Learning. Springer, New York, NY. https://doi.org/10.1007/978-1-4939-0530-0_14

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  • DOI: https://doi.org/10.1007/978-1-4939-0530-0_14

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