MATAR: Keywords Enhanced Multi-label Learning for Tag Recommendation

  • Licheng LiEmail author
  • Yuan Yao
  • Feng Xu
  • Jian Lu
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9313)


Tagging is a popular way to categorize and search online content, and tag recommendation has been widely studied to better support automatic tagging. In this work, we focus on recommending tags for content-based applications such as blogs and question-answering sites. Our key observation is that many tags actually have appeared in the content in these applications. Based on this observation, we first model the tag recommendation problem as a multi-label learning problem and then further incorporate keyword extraction to improve recommendation accuracy. Moreover, we speedup the proposed method using a locality-sensitive hashing strategy. Experimental evaluations on two real data sets demonstrate the effectiveness and efficiency of our proposed methods.


Tag recommendation multi-label learning keyword extraction locality-sensitive hashing 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Andoni, A., Indyk, P.: Near-optimal hashing algorithms for approximate nearest neighbor in high dimensions. In: 47th Annual IEEE Symposium on Foundations of Computer Science, FOCS 2006, pp. 459–468. IEEE (2006)Google Scholar
  2. 2.
    Belém, F., Martins, E., Pontes, T., Almeida, J., Gonçalves, M.: Associative tag recommendation exploiting multiple textual features. In: SIGIR, pp. 1033–1042. ACM (2011)Google Scholar
  3. 3.
    Feng, W., Wang, J.: Incorporating heterogeneous information for personalized tag recommendation in social tagging systems. In: Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1276–1284. ACM (2012)Google Scholar
  4. 4.
    Jäschke, R., Marinho, L., Hotho, A., Schmidt-Thieme, L., Stumme, G.: Tag recommendations in social bookmarking systems. AI Communication 21(4), 231–247 (2008)MathSciNetzbMATHGoogle Scholar
  5. 5.
    Krestel, R., Fankhauser, P., Nejdl, W.: Latent dirichlet allocation for tag recommendation. In: Proceedings of the Third ACM Conference on Recommender Systems, pp. 61–68. ACM (2009)Google Scholar
  6. 6.
    Lipczak, M., Milios, E.: Learning in efficient tag recommendation. In: Proceedings of the Fourth ACM Conference on Recommender Systems, pp. 167–174. ACM (2010)Google Scholar
  7. 7.
    Liu, R., Niu, Z.: A collaborative filtering recommendation algorithm based on tag clustering. In: Park, J.J.(J.H.), Stojmenovic, I., Choi, M., Xhafa, F. (eds.) Future Information Technology. LNEE, vol. 276, pp. 177–183. Springer, Heidelberg (2014)CrossRefGoogle Scholar
  8. 8.
    Lu, Y.T., Yu, S.I., Chang, T.C., Hsu, J.Y.J.: A content-based method to enhance tag recommendation. In: IJCAI, vol. 9, pp. 2064–2069 (2009)Google Scholar
  9. 9.
    Mihalcea, R., Tarau, P.: Textrank: Bringing order into texts. Association for Computational Linguistics (2004)Google Scholar
  10. 10.
    Murfi, H., Obermayer, K.: A two-level learning hierarchy of concept based keyword extraction for tag recommendations. In: ECML PKDD Discovery Challenge (DC 2009), p. 201 (2009)Google Scholar
  11. 11.
    Rendle, S., Balby Marinho, L., Nanopoulos, A., Schmidt-Thieme, L.: Learning optimal ranking with tensor factorization for tag recommendation. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 727–736. ACM (2009)Google Scholar
  12. 12.
    Rendle, S., Schmidt-Thieme, L.: Pairwise interaction tensor factorization for personalized tag recommendation. In: Proceedings of the Third ACM International Conference on Web Search and Data Mining, pp. 81–90. ACM (2010)Google Scholar
  13. 13.
    Saha, A.K., Saha, R.K., Schneider, K.A.: A discriminative model approach for suggesting tags automatically for stack overflow questions. In: Proceedings of the 10th Working Conference on Mining Software Repositories, pp. 73–76. IEEE Press (2013)Google Scholar
  14. 14.
    Si, X., Sun, M.: Tag-lda for scalable real-time tag recommendation. Journal of Computational Information Systems 6(1), 23–31 (2009)CrossRefGoogle Scholar
  15. 15.
    Sigurbjörnsson, B., Van Zwol, R.: Flickr tag recommendation based on collective knowledge. In: Proceedings of the 17th International Conference on World Wide Web, pp. 327–336. ACM (2008)Google Scholar
  16. 16.
    Song, Y., Zhang, L., Giles, C.L.: Automatic tag recommendation algorithms for social recommender systems. ACM Transactions on the Web (TWEB) 5(1), 4 (2011)Google Scholar
  17. 17.
    Subramaniyaswamy, V., Pandian, S.C.: Topic ontology-based efficient tag recommendation approach for blogs. International Journal of Computational Science and Engineering 9(3), 177–187 (2014)CrossRefGoogle Scholar
  18. 18.
    Symeonidis, P., Nanopoulos, A., Manolopoulos, Y.: Tag recommendations based on tensor dimensionality reduction. In: Proceedings of the 2008 ACM Conference on Recommender Systems, pp. 43–50. ACM (2008)Google Scholar
  19. 19.
    Wang, J., Hong, L., Davison, B.D.: Rsdc’09: Tag recommendation using keywords and association rules. In: ECML PKDD Discovery Challenge, pp. 261–274 (2009)Google Scholar
  20. 20.
    Wang, T., Wang, H., Yin, G., Ling, C.X., Li, X., Zou, P.: Tag recommendation for open source software. Frontiers of Computer Science 8(1), 69–82 (2014)MathSciNetCrossRefGoogle Scholar
  21. 21.
    Xia, X., Lo, D., Wang, X., Zhou, B.: Tag recommendation in software information sites. In: Proceedings of the 10th Working Conference on Mining Software Repositories, pp. 287–296. IEEE Press (2013)Google Scholar
  22. 22.
    Zhang, M.L., Zhou, Z.H.: Ml-knn: A lazy learning approach to multi-label learning. Pattern Recognition 40(7), 2038–2048 (2007)CrossRefzbMATHGoogle Scholar

Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  1. 1.State Key Laboratory for Novel Software TechnologyNanjing UniversityNanjingChina

Personalised recommendations