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A Federated Recommender System for Online Learning Environments

  • Lei Zhou
  • Sandy El Helou
  • Laurent Moccozet
  • Laurent Opprecht
  • Omar Benkacem
  • Christophe Salzmann
  • Denis Gillet
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7558)

Abstract

From e-commerce to social networking sites, recommender systems are gaining more and more interest. They provide connections, news, resources, or products of interest. This paper presents a federated recommender system, which exploits data from different online learning platforms and delivers personalized recommendation. The underlying educational objective is to enable academic institutions to provide a Web 2.0 dashboard bringing together open resources from the Cloud and proprietary content from in-house learning management systems. The paper describes the main aspects of the federated recommender system, including its adopted architecture, the common data model used to harvest the different learning platforms, the recommendation algorithm, as well as the recommendation display widget.

Keywords

Technology-Enhanced Learning Personal Learning Environments Federated Recommender System Web 2.0 

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References

  1. 1.
    Guy, I., Jaimes, A., Agulló, P., Moore, P., Nandy, P., Nastar, C., Schinzel, H.: Will Recommenders Kill Search? Recommender Systems – an Industry Perspective. In: Proceedings of the 4th ACM Conference on Recommender Systems, pp. 7–12 (2010)Google Scholar
  2. 2.
    Manouselis, N., Drachsler, H., Vuorikari, R., Hummel, H., Koper, R.: Recommender Systems in Technology Enhanced Learning. In: Recommender Systems Handbook, Part 2, pp. 387–415 (2011)Google Scholar
  3. 3.
    Dabbagh, N., Kitsantas, A.: Personal Learning Environments, Social Media, and Self Regulated Learning: A Natural Formula for Connecting Formal and Informal Learning. The Internet and Higher Education 15, 3–8 (2012)CrossRefGoogle Scholar
  4. 4.
    Tang, T.Y., Mccalla, G.: Smart Recommendation for an Evolving E-Learning System: Architecture and Experiment. International Journal on E-Learning 4, 105–129, http://www.editlib.org/p/5822 (retrieved March 2012)
  5. 5.
    LMS Installations 2010 at Swiss Institutions of Higher Education, http://eduhub.ch/info/lms-installations10.html (retrieved April 2012)
  6. 6.
    Drbálek, Z., Dulík, T., Koblischke, R.: Developing components for distributed search engine ObjectSpot. In: Proceedings of the 8th WSEAS International Conference on Distance Learning and Web Engineering, pp. 82–85 (2008)Google Scholar
  7. 7.
    Govaerts, S., El Helou, S., Duval, E., Gillet, D.: A Federated Search and Social Recommendation Widget. In: Proceedings of the 2nd International Workshop on Social Recommender Systems in conjunction with the 2011 ACM Conference on Computer Supported Cooperative Work, pp. 1–8 (2011)Google Scholar
  8. 8.
    Kaushik, S., Kollipalli, D.: Multi-Agent based Architecture for Querying Disjoint Data Repositories. In: International Conference on Machine and Web Intelligence, pp. 28–34 (2011)Google Scholar
  9. 9.
    Gil, A.B., De la Prieta, F., Rodríguez, S.: Automatic Learning Object Extraction and Classification in Heterogeneous Environments. In: Pérez, J.B., Corchado, J.M., Moreno, M.N., Julián, V., Mathieu, P., Canada-Bago, J., Ortega, A., Caballero, A.F. (eds.) Highlights in Practical Applications of Agents and Multiagent Systems. AISC, vol. 89, pp. 109–116. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  10. 10.
    Ternier, S., Verbert, K., Parra, G., Vandeputte, B., Klerkx, J., Duval, E., Ordoez, V., Ochoa, X.: The Ariadne Infrastructure for Managing and Storing Metadata. IEEE Internet Computing 13, 18–25 (2009)CrossRefGoogle Scholar
  11. 11.
    Ha, K.-H., Niemann, K., Schwertel, U., Holtkamp, P., Pirkkalainen, H., Boerner, D., Kalz, M., Pitsilis, V., Vidalis, A., Pappa, D., Bick, M., Pawlowski, J., Wolpers, M.: A Novel Approach towards Skill-Based Search and Services of Open Educational Resources. In: García-Barriocanal, E., Cebeci, Z., Okur, M.C., Öztürk, A. (eds.) MTSR 2011. CCIS, vol. 240, pp. 312–323. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  12. 12.
    Grewe, L., Pandey, S.: Quantization of Social Data for Friend Advertisement Recommendation System. In: Nagamalai, D. (ed.) PDCTA 2011. CCIS, vol. 203, pp. 596–614. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  13. 13.
    El Helou, S., Li, N., Gillet, D.: The 3A interaction model: towards bridging the gap between formal and informal learning. In: Proceedings of the Third International Conferences on Advances in Computer-Human Interactions, pp. 179–184 (2010)Google Scholar
  14. 14.
    Page, L., Brin, S., Motwani, R., Winograd, T.: The PageRank citation ranking: Bringing order to the Web. Technical report. Stanford: Stanford Digital Library Technologies Project (1999)Google Scholar
  15. 15.
    El Helou, S., Salzmann, C., Gillet, D.: The 3A Personalized, Contextual and Relation based Recommender System. Journal of Universal Computer Science 16(16), 2179–2195 (2010)Google Scholar
  16. 16.
    Shibboleth Architecture Technical Overview, http://shibboleth.internet2.edu/docs/draft-mace-shibboleth-tech-overview-latest.pdf (retrieved March 2012)

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Lei Zhou
    • 1
  • Sandy El Helou
    • 2
  • Laurent Moccozet
    • 3
  • Laurent Opprecht
    • 3
  • Omar Benkacem
    • 3
  • Christophe Salzmann
    • 2
  • Denis Gillet
    • 2
  1. 1.Tongji UniversityShanghaiChina
  2. 2.Ecole Polytechnique Fédérale de Lausanne (EPFL)LausanneSwitzerland
  3. 3.University of Geneva (UNIGE)GenevaSwitzerland

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