Advertisement

Introduction

  • Shu WuEmail author
  • Qiang Liu
  • Liang Wang
  • Tieniu Tan
Chapter
Part of the SpringerBriefs in Computer Science book series (BRIEFSCOMPUTER)

Abstract

In this chapter, we introduce the basic concepts of contextual information and collaborative prediction. Then, we introduce the scenarios of context-aware collaborative prediction and point out some limitations of the conventional methods. Finally, we introduce the tasks of collaborative prediction, on which we will compare the performance of our methods and conventional methods.

References

  1. 1.
    Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Recommender Systems Handbook, pp. 217–253. Springer, Berlin (2011)Google Scholar
  2. 2.
    Agarwal, D., Chen, B.C.: Regression-based latent factor models. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 19–28. ACM, New York (2009)Google Scholar
  3. 3.
    Karatzoglou, A., Amatriain, X., Baltrunas, L., Oliver, N.: Multiverse recommendation: n-dimensional tensor factorization for context-aware collaborative filtering. In: Proceedings of the fourth ACM conference on Recommender systems, pp. 79–86. ACM, New York (2010)Google Scholar
  4. 4.
    Kim, Y., Choi, S.: Bayesian binomial mixture model for collaborative prediction with non-random missing data. In: Eighth ACM Conference on Recommender Systems, RecSys ’14, Foster City, Silicon Valley, CA, USA, pp. 201–208, 06–10 Oct 2014Google Scholar
  5. 5.
    Liu, Q., Wu, S., Wang, L.: Cot: Contextual operating tensor for context-aware recommender systems. In: Proceedings of the 29th International AAAI Conference on Artificial Intelligence, pp. 203–209. AAAI, California (2015)Google Scholar
  6. 6.
    McMahan, H.B., Holt, G., Sculley, D., Young, M., Ebner, D., Grady, J., Nie, L., Phillips, T., Davydov, E., Golovin, D., et al.: Ad click prediction: a view from the trenches. In: Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining, pp. 1222–1230. ACM, New York (2013)Google Scholar
  7. 7.
    Oentaryo, R.J., Lim, E.P., Low, J.W., Lo, D., Finegold, M.: Predicting response in mobile advertising with hierarchical importance-aware factorization machine. In: Proceedings of the 7th ACM international conference on Web search and data mining, pp. 123–132. ACM, New York (2014)Google Scholar
  8. 8.
    Palmisano, C., Tuzhilin, A., Gorgoglione, M.: Using context to improve predictive modeling of customers in personalization applications. IEEE Trans. Knowl. Data Eng. 20(11), 1535–1549 (2008)Google Scholar
  9. 9.
    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, New York (2009)Google Scholar
  10. 10.
    Rendle, S., Gantner, Z., Freudenthaler, C., Schmidt-Thieme, L.: Fast context-aware recommendations with factorization machines. In: Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval, pp. 635–644. ACM, New York (2011)Google Scholar
  11. 11.
    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, New York (2010)Google Scholar
  12. 12.
    Rish, I., Tesauro, G.: Active collaborative prediction with maximum margin matrix factorization. In: International Symposium on Artificial Intelligence and Mathematics, ISAIM 2008, Fort Lauderdale, Florida, USA, 2–4 Jan 2008Google Scholar
  13. 13.
    Shi, Y., Karatzoglou, A., Baltrunas, L., Larson, M., Hanjalic, A.: Cars2: Learning context-aware representations for context-aware recommendations. In: Proceedings of the 23rd ACM International Conference on Conference on Information and Knowledge Management, pp. 291–300. ACM, New York (2014)Google Scholar
  14. 14.
    Stern, D.H., Herbrich, R., Graepel, T.: Matchbox: Large scale online bayesian recommendations. In: Proceedings of the 18th International Conference on World Wide Web, pp. 111–120. International World Wide Web Conferences Steering Committee, Geneva (2009)Google Scholar
  15. 15.
    Xu, M., Zhu, J., Zhang, B.: Nonparametric max-margin matrix factorization for collaborative prediction. In: 26th Annual Conference on Neural Information Processing Systems, pp. 64–72. (2012)Google Scholar
  16. 16.
    Yan, L., Li, W.J., Xue, G.R., Han, D.: Coupled group lasso for web-scale ctr prediction in display advertising. In: Proceedings of the 31th International Conference on Machine Learning, pp. 802–810. ACM, New York (2014)Google Scholar

Copyright information

© The Author(s) 2017

Authors and Affiliations

  1. 1.National Laboratory of Pattern Recognition, Institute of AutomationChinese Academy of SciencesBeijingChina

Personalised recommendations