Cross Media Recommendation in Digital Library

  • Jia Zhang
  • Zhenming Yuan
  • Kai Yu
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8839)


Rapidly increasing volumes of heterogeneous media digital contents are produced into the digital library by the forms of the digital books, videos, images, etc. However, traditional recommendation approaches in the digital library cannot support the potential semantic connections across different types of media data. In this paper, a cross-media recommendation algorithm for the digital library is proposed, in which the retrieved items may come from different data sources, and the results do not need to be of the same media type the user ever read or tagged. Firstly, a fused user-item-feature tensor is used to represent the cross-media data set. Then the item-context latent space and item-user rating latent space are reconstructed by TUCKER based tensor decomposition. And the structural grouping sparsity approach is used to select the feature groups and the subset of homogeneous features in one group, which can deal with the difficulty of sparse and high dimension of the big feature matrix. Finally, the Top-n items are recommended according to the prediction probability estimated. Experiments conducted on a cross-media dataset based on China Academic Digital Associative Library (CADAL). The performances evaluation is based on the recall precision and diversity score. The experiment results show that our approach has good recommendation accuracy as well as good diversity.


Cross-media Recommendation Feature Selection Sparse Representation CADAL 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Wu, F., Lu, X., Zhang, Z., Yan, S., Rui, Y., Zhuang, Y.: Cross-media semantic representation via bi-directional learning to rank. In: Proceedings of the 21st ACM International Conference on Multimedia (MM 2013), pp. 877–886. ACM, New York (2013)CrossRefGoogle Scholar
  2. 2.
    Zhuang, Y., Yang, Y., Wu, F.: Mining semantic correlation of heterogeneous multimedia data for cross-media retrieval. IEEE Transactions on Multimedia 10(2), 221–229 (2008)CrossRefGoogle Scholar
  3. 3.
    Ricci, F., Rokach, L., Shapira, B., Kantor, P.: Recommender Systems Handbook. Springer (2010)Google Scholar
  4. 4.
    Pazzani, M.J., Billsus, D.: Content-based recommendation systems. In: Brusilovsky, P., Kobsa, A., Nejdl, W. (eds.) Adaptive Web 2007. LNCS, vol. 4321, pp. 325–341. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  5. 5.
    Koren, Y., Bell, R.: Advances in collaborative filtering. In: Recommender Systems Handbook. Springer (2010)Google Scholar
  6. 6.
    Koren, Y., Bell, R., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42(8), 30–37 (2009)CrossRefGoogle Scholar
  7. 7.
    Funk, S.: Netflix update: Try this at home (2006),
  8. 8.
    Wu, F., Han, Y.H., Liu, X., Shao, J., Zhuang, Y.T., Zhang, Z.F.: The Heterogeneous feature selection with structural sparsity for multimedia annotation and hashing: A survey. International Journal of Multimedia Information Retrieval 1(1), 3–15 (2012)CrossRefGoogle Scholar
  9. 9.
    Yuan, Z., Yu, K., Zhang, J., Pan, H.: Structural context-aware cross media recommendation. In: Lin, W., Xu, D., Ho, A., Wu, J., He, Y., Cai, J., Kankanhalli, M., Sun, M.-T. (eds.) PCM 2012. LNCS, vol. 7674, pp. 790–800. Springer, Heidelberg (2012)CrossRefGoogle Scholar
  10. 10.
    Tucker, L.R.: Some mathematical notes on three-mode factor analysis. Psychometrika 31(3), 279–311 (1996)CrossRefGoogle Scholar
  11. 11.
    Harshman, R.A.: Foundations of the PARAFAC procedure: models and conditions for an “explanatory” multimodal factor analysis. University of California at Los Angeles (1970)Google Scholar
  12. 12.
    Tibshirani, R.: Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B (Statistical Approachology) 58(1), 267–288 (1996)MathSciNetzbMATHGoogle Scholar
  13. 13.
    Yuan, M., Lin, Y.: Model selection and estimation in regression with grouped variables. J. R. Stat. Soc. Ser. B (Approachological) 68(1), 49–67 (2006)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Wu, F., Han, Y., Tian, Q., Zhuang, Y.: Multi-label boosting for image annotation by structural grouping sparsity. In: Proceedings of the 2010 ACM International Conference on Multimedia (ACMMM), New York, NY, USA, pp. 15–24 (2010)Google Scholar
  15. 15.
    Ricci, F., Rokach, L., Shapira, B., Kantor, P.B. (eds.): Recommender Systems Handbook, 1st edn. Springer (2011)Google Scholar
  16. 16.
    Hill, W., Stead, L., Rosenstein, M., Furnas, G.: Recommending and evaluating choices in a virtual community of use. In: Proc. of the SIGCHI Conference on Human Factors in Computing Systems (CHI 1995), pp. 194–201 (1995)Google Scholar
  17. 17.
    Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information tapestry. Communications of the ACM 35(12), 61–70 (1992)CrossRefGoogle Scholar
  18. 18.
    Resnick, P., Iacovou, N., Suchak, M., Bergstorm, P., Riedl, J.: An open architecture for collaborative filtering of netnews. In: Proc. of the ACM Conference on Computer Supported Cooperative Work (CSCW 1994), pp. 175–186 (1994)Google Scholar
  19. 19.
    Adomavicius, G., Kwon, Y.O.: New recommendation techniques for multi-criteria rating systems. IEEE Intelligent Systems 22(3), 48–55 (2007)CrossRefGoogle Scholar
  20. 20.
    Bell, R., Bennett, J., Koren, Y., Volinsky, C.: The million dollar programming prize. IEEE Spectrum 46(5), 28–33 (2009)CrossRefGoogle Scholar
  21. 21.
    Schein, A.I., Popescul, A., Ungar, L.H., Pennock, D.M.: Methods and metrics for cold-start recommendations. In: Proc. of the 25th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2002), pp. 253-260 (2002)Google Scholar
  22. 22.
    Lops, P., de Gemmis, M., Semeraro, G.: Content based recommender systems: state of the art and trends. In: Recommender Systems Handbook. Springer (2010)Google Scholar
  23. 23.
    Burke, R.: Interactive critiquing for catalog navigation in e-commerce. Artificial Intelligence Review 18(3-4), 245–267 (2002)CrossRefGoogle Scholar
  24. 24.
    Schclar, A., Tsikinovsky, A., Rokach, L., Meisels, A., Antwarg, L.: Ensemble methods for improving the performance of neighborhood-based collaborative filtering. In: Proc. of the 3th ACM Conference on Recommender Systems, pp. 261–264 (2009)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Jia Zhang
    • 1
    • 2
  • Zhenming Yuan
    • 2
  • Kai Yu
    • 2
  1. 1.State Key Laboratory of Digital Publishing TechnologyBeijingChina
  2. 2.School of Information Science and EngineeringHangzhou Normal UniversityHangzhouChina

Personalised recommendations