Improving Tag Recommendation Using Few Associations

  • Matthijs van Leeuwen
  • Diyah Puspitaningrum
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7619)


Collaborative tagging services allow users to freely assign tags to resources. As the large majority of users enters only very few tags, good tag recommendation can vastly improve the usability of tags for techniques such as searching, indexing, and clustering. Previous research has shown that accurate recommendation can be achieved by using conditional probabilities computed from tag associations. The main problem, however, is that enormous amounts of associations are needed for optimal recommendation.

We argue and demonstrate that pattern selection techniques can improve tag recommendation by giving a very favourable balance between accuracy and computational demand. That is, few associations are chosen to act as information source for recommendation, providing high-quality recommendation and good scalability at the same time.

We provide a proof-of-concept using an off-the-shelf pattern selection method based on the Minimum Description Length principle. Experiments on data from Delicious, LastFM and YouTube show that our proposed methodology works well: applying pattern selection gives a very favourable trade-off between runtime and recommendation quality.


Association Rule Pattern Selection Minimum Support Threshold Code Table Recommendation Quality 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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  1. 1.
    Balby Marinho, L., Hotho, A., Jäschke, R., Nanopoulos, A., Rendle, S., Schmidt-Thieme, L., Stumme, G., Symeonidis, P.: Recommender Systems for Social Tagging Systems. Springer (February 2012)Google Scholar
  2. 2.
    Begelman, G.: Automated tag clustering: Improving search and exploration in the tag space. In: Proc of the WWW 2006 (2006)Google Scholar
  3. 3.
    Han, J., Pei, J.: Mining frequent patterns by pattern-growth: methodology and implications. SIGKDD Explorations Newsletter 2(2), 14–20 (2000)CrossRefGoogle Scholar
  4. 4.
    Heymann, P., Ramage, D., Garcia-Molina, H.: Social tag prediction. In: Proc. of the SIGIR 2008, pp. 531–538 (2008)Google Scholar
  5. 5.
    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
  6. 6.
    Jäschke, R., Marinho, L., Hotho, A., Schmidt-Thieme, L., Stumme, G.: Tag Recommendations in Folksonomies. In: Kok, J.N., Koronacki, J., Lopez de Mantaras, R., Matwin, S., Mladenič, D., Skowron, A. (eds.) PKDD 2007. LNCS (LNAI), vol. 4702, pp. 506–514. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  7. 7.
    Law, E., Settles, B., Mitchell, T.: Learning to Tag from Open Vocabulary Labels. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds.) ECML PKDD 2010, Part II. LNCS, vol. 6322, pp. 211–226. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  8. 8.
    van Leeuwen, M., Bonchi, F., Sigurbjörnsson, B., Siebes, A.: Compressing tags to find interesting media groups. In: Proc of the CIKM 2009, pp. 1147–1156 (2009)Google Scholar
  9. 9.
    Li, X., Guo, L., Zhao, Y.E.: Tag-based social interest discovery. In: Proc. of the WWW 2008, pp. 675–684 (2008)Google Scholar
  10. 10.
    Lipczak, M., Milios, E.E.: Learning in efficient tag recommendation. In: Proc. of the RecSys 2010, pp. 167–174 (2010)Google Scholar
  11. 11.
    Menezes, G.V., Almeida, J.M., Belém, F., Gonçalves, M.A., Lacerda, A., de Moura, E.S., Pappa, G.L., Veloso, A., Ziviani, N.: Demand-Driven Tag Recommendation. In: Balcázar, J.L., Bonchi, F., Gionis, A., Sebag, M. (eds.) ECML PKDD 2010, Part II. LNCS, vol. 6322, pp. 402–417. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  12. 12.
    Rae, A., Sigurbjörnsson, B., van Zwol, R.: Improving tag recommendation using social networks. In: Proc of the RIAO 2010, pp. 92–99 (2010)Google Scholar
  13. 13.
    Siebes, A., Vreeken, J., van Leeuwen, M.: Item sets that compress. In: Proc. of the SDM 2006, pp. 393–404 (2006)Google Scholar
  14. 14.
    Sigurbjörnsson, B., van Zwol, R.: Flickr tag recommendation based on collective knowledge. In: WWW, pp. 327–336 (2008)Google Scholar
  15. 15.
    Smets, K., Vreeken, J.: Slim: Directly mining descriptive patterns. In: Proc. of the SDM 2012 (2012)Google Scholar
  16. 16.
    Song, Y., Zhuang, Z., Li, H., Zhao, Q., Li, J., Lee, W.-C., Lee Giles, C.: Real-time automatic tag recommendation. In: Proc of the SIGIR 2008, pp. 515–522 (2008)Google Scholar
  17. 17.
    Toderici, G., Aradhye, H., Pasca, M., Sbaiz, L., Yagnik, J.: Finding meaning on youtube: Tag recommendation and category discovery. In: CVPR, pp. 3447–3454 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Matthijs van Leeuwen
    • 1
    • 2
  • Diyah Puspitaningrum
    • 1
  1. 1.Dept. of Information & Computing SciencesUniversiteit UtrechtThe Netherlands
  2. 2.Dept. of Computer ScienceKU LeuvenBelgium

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