Unsupervised Auto-tagging for Learning Object Enrichment

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6964)


An online presence is gradually becoming an essential part of every learning institute. As such, a large portion of learning material is becoming available online. Incongruently, it is still a challenge for authors and publishers to guarantee accessibility, support effective retrieval and the consumption of learning objects. One reason for this is that non-annotated learning objects pose a major problem with respect to their accessibility. Non-annotated objects not only prevent learners from finding new information; but also hinder a system’s ability to recommend useful resources. To address this problem, commonly known as the cold-start problem, we automatically annotate specific learning resources using a state-of-the-art automatic tag annotation method: α-TaggingLDA, which is based on the Latent Dirichlet Allocation probabilistic topic model. We performed a user evaluation with 115 participants to measure the usability and effectiveness of α-TaggingLDA in a collaborative learning environment. The results show that automatically generated tags were preferred 35% more than the original authors’ annotations. Further, they were 17.7% more relevant in terms of recall for users. The implications of these results is that automatic tagging can facilitate effective information access to relevant learning objects.


Metadata Generation User Study LDA Cold-Start Recommender Systems 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Abel, F., Marenzi, I., Nejdl, W., Zerr, S.: Sharing distributed resources in learnWeb2.0. In: Cress, U., Dimitrova, V., Specht, M. (eds.) EC-TEL 2009. LNCS, vol. 5794, pp. 154–159. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  2. 2.
    Atkins, D.E., Seely, B.J., Allen, H.: A review of the open educational resources ( oer ) movement: Achievements, challenges and New Opportunities. Review Literature And Arts Of The Americas, 84 (2007)Google Scholar
  3. 3.
    Bischoff, K., Firan, C.S., Nejdl, W., Paiu, R.: Can all tags be used for search? In: CIKM 2008: Proceeding of the 17th ACM conference on Information and knowledge management, pp. 193–202. ACM Press, New York (2008)Google Scholar
  4. 4.
    Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent dirichlet allocation. Journal of Machine Learning Research 3, 993–1022 (2003)zbMATHGoogle Scholar
  5. 5.
    Diaz-Aviles, E., Georgescu, M., Stewart, A., Nejdl, W.: Lda for on-the-fly auto tagging. In: Proceedings of the fourth ACM conference on Recommender systems, RecSys2010, pp. 309–312. ACM Press, New York (2010)CrossRefGoogle Scholar
  6. 6.
    Drachsler, H., Pecceu, D., Arts, T., Hutten, E., Rutledge, L., van Rosmalen, P., Hummel, H., Koper, R.: ReMashed – recommendations for mash-up personal learning environments. In: Cress, U., Dimitrova, V., Specht, M. (eds.) EC-TEL 2009. LNCS, vol. 5794, pp. 788–793. Springer, Heidelberg (2009)CrossRefGoogle Scholar
  7. 7.
    Drachsler, H., Rutledge, L., van Rosmalen, P., Hummel, H.G.K., Pecceu, D., Arts, T., Hutten, E., Koper, R.: Remashed - an usability study of a recommender system for mash-ups for learning. iJET 5(S1), 7–11 (2010)Google Scholar
  8. 8.
    Griffiths, T.L., Steyvers, M.: Finding scientific topics. Proc. Natl. Acad. Sci. U S A 101(suppl. 1), 5228–5235 (2004)CrossRefGoogle Scholar
  9. 9.
    Herlocker, J.L., Konstan, J.A., Terveen, L.G., Riedl, J.T.: Evaluating collaborative filtering recommender systems. ACM Trans. Inf. Syst. 22, 5–53 (2004)CrossRefGoogle Scholar
  10. 10.
    Hotho, A., Jäschke, R., Schmitz, C., Stumme, G.: Information retrieval in folksonomies: Search and ranking. In: Sure, Y., Domingue, J. (eds.) ESWC 2006. LNCS, vol. 4011, pp. 411–426. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  11. 11.
    Krestel, R., Fankhauser, P.: Language models and topic models for personalizing tag recommendation. In: 2010 IEEE WIC ACM International Conference on Web Intelligence and Intelligent Agent Technology, pp. 82–89 (2010)Google Scholar
  12. 12.
    Krestel, R., Fankhauser, P., Nejdl, W.: Latent dirichlet allocation for tag recommendation. In: Proceedings of the third ACM conference on Recommender systems, RecSys 2009, pp. 61–68. ACM Press, New York (2009)Google Scholar
  13. 13.
    Lohmann, S., Thalmann, S., Harrer, A., Maier, R.: Learner-generated annotation of learning resources - lessons from experiments on tagging. In: International Conference on Knowledge Management (I-KNOW 2008), Graz, Austria (September 2008)Google Scholar
  14. 14.
    McCallum, A.K.: Mallet: A machine learning for language toolkit (2002),
  15. 15.
    Niemann, K., Schwertel, U., Kalz, M., Mikroyannidis, A., Fisichella, M., Friedrich, M., Dicerto, M., Ha, K.-H., Holtkamp, P., Kawase, R., Parodi, E., Pawlowski, J., Pirkkalainen, H., Pitsilis, V., Vidalis, A., Wolpers, M., Zimmermann, V.: Skill-based scouting of open management content. In: Wolpers, M., Kirschner, P.A., Scheffel, M., Lindstaedt, S., Dimitrova, V. (eds.) EC-TEL 2010. LNCS, vol. 6383, pp. 632–637. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  16. 16.
    Rendle, S., Balby Marinho, L., Nanopoulos, A., Lars, S.-T.: Learning optimal ranking with tensor factorization for tag recommendation. In: Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining, KDD 2009, pp. 727–736. ACM Press, New York (2009)Google Scholar
  17. 17.
    Rendle, S., Lars, S.-T.: Pairwise interaction tensor factorization for personalized tag recommendation. In: Proceedings of the third ACM international conference on Web search and data mining, WSDM 2010, pp. 81–90. ACM Press, New York (2010)Google Scholar
  18. 18.
    Sen, S., Vig, J., Riedl, J.: Tagommenders: Connecting users to items through tags. In: International World Wide Web Conference, Madrid, Spain, April 20. ACM Press, New York (2009)Google Scholar
  19. 19.
    Stewart, A., Diaz-Aviles, E., Nejdl, W., Marinho, L.B., Nanopoulos, A., Schmidt-Thieme, L.: Cross-tagging for personalized open social networking. In: Cattuto, C., Ruffo, G., Menczer, F. (eds.) Proceedings of the 20th ACM Conference on Hypertext and Hypermedia (Hypertext 2009), Torino, Italy, pp. 271–278. ACM Press, New York (2009)CrossRefGoogle Scholar
  20. 20.
    Symeonidis, P., Nanopoulos, A., Manolopoulos, Y.: Tag recommendations based on tensor dimensionality reduction. In: Proceedings of the 2008 ACM conference on Recommender systems, RecSys 2008, pp. 43–50. ACM Press, New York (2008)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  1. 1.L3S Research CenterLeibniz University HannoverGermany

Personalised recommendations