Jean-Francois Pessiot, Tuong-Vinh Truong, Nicolas Usunier, Massih-Reza Amini, Patrick Gallinari



Up to now, most contributions to collaborative filtering rely on rating prediction to generate the recommendations. We, instead, try to correctly rank the items according to the users’ tastes. First, we define a ranking error function which takes available pairwise preferences between items into account. Then we design an effective algorithm that optimizes this error. Finally we illustrate the proposal on a standard collaborative filtering dataset. We adapted the evaluation protocol proposed by (Marlin, 2004) for rating prediction based systems to our case, where pairwise preferences are predicted instead. The preliminary results are between those of two reference rating prediction based methods. We suggest different directions to further explore our ranking based approach for collaborative filtering.


  1. Amini, M.-R. and Gallinari, P. (2003). Semi-supervised learning with explicit misclassification modeling. In Gottlob, G. and Walsh, T., editors, IJCAI, pages 555- 560. Morgan Kaufmann.
  2. Amini, M.-R., Usunier, N., and Gallinari, P. (2005). Automatic text summarization based on word-clusters and ranking algorithms. In Proceedings of the 27th European Conference on IR Research, ECIR 2005, Santiago de Compostela, Spain, March 21-23 , Lecture Notes in Computer Science, pages 142-156. Springer.
  3. Ando and Zhang (2005). A framework for learning predictive structures from multiple tasks and unlabeled data. Journal of Machine Learning Research.
  4. Breese, J. S., Heckerman, D., and Kadie, C. (1998). Empirical analysis of predictive algorithms for collaborative filtering. Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence.
  5. Caillet, M., Pessiot, J.-F., Amini, M.-R., and Gallinari, P. (2004). Unsupervised learning with term clustering for thematic segmentation of texts. In Proceedings of the 7th Recherche d'Information Assiste par Ordinateur, Avignon, France, pages 648-656. CID.
  6. Canny, J. (2002). Collaborative filtering with privacy via factor analysis. Proceedings of the 25th annual international ACM SIGIR conference on Research and development in information retrieval.
  7. Chee, S., Han, J., and Wang, K. (2001). Rectree: An efficient collaborative filtering method. In Data Warehousing and Knowledge Discovery.
  8. Deshpande, M. and Karypis, G. (2004). Item-based topn recommendation algorithms. ACM Transactions on Information Systems (TOIS).
  9. Dhillon, I. S. and Sra, S. (2006). Generalized nonnegative matrix approximations with bregman divergences. NIPS.
  10. Herlocker, J., Konstan, J., and Riedl, J. (1999). An algorithmic framework for performing collaborative filtering.
  11. Herlocker, J., Konstan, J., Terveen, L., and Riedl, J. (2004). Evaluating collaborative filtering recommender systems. ACM Transactions on Information Systems.
  12. Hofmann, T. (2004). Latent semantic models for collaborative filtering. ACM Trans. Inf. Syst., 22(1):89-115.
  13. Lee, D. D. and Seung, H. S. (1999). Learning the parts of objects by non-negative matrix factorization. Nature.
  14. Marlin, B. (2003). Modeling user rating profiles for collaborative filtering. Advances in Neural Information Processing Systems.
  15. Marlin, B. (2004). Collaborative filtering: A machine learning perspective.
  16. Pessiot, J.-F., Truong, V., Usunier, N., Amini, M., and Gallinari, P. (2006). Factorisation en matrices nonnegatives pour le filtrage collaboratif. In 3eme Conference en Recherche d'Information et Applications (CORIA'06), pages 315-326, Lyon.
  17. Srebro, N. and Jaakkola, T. (2003). Weighted low rank approximation. In ICML 7803. Proceedings of the 20th international conference on machine learning.
  18. Srebro, N., Rennie, J. D. M., and Jaakkola, T. S. (2004). Maximum-margin matrix factorization. In Saul, L. K., Weiss, Y., and Bottou, l., editors, Advances in Neural Information Processing Systems 17. MIT Press, Cambridge, MA.

Paper Citation

in Harvard Style

Pessiot J., Truong T., Usunier N., Amini M. and Gallinari P. (2007). LEARNING TO RANK FOR COLLABORATIVE FILTERING . In Proceedings of the Ninth International Conference on Enterprise Information Systems - Volume 2: ICEIS, ISBN 978-972-8865-89-4, pages 145-151. DOI: 10.5220/0002396301450151

in Bibtex Style

author={Jean-Francois Pessiot and Tuong-Vinh Truong and Nicolas Usunier and Massih-Reza Amini and Patrick Gallinari},
booktitle={Proceedings of the Ninth International Conference on Enterprise Information Systems - Volume 2: ICEIS,},

in EndNote Style

JO - Proceedings of the Ninth International Conference on Enterprise Information Systems - Volume 2: ICEIS,
SN - 978-972-8865-89-4
AU - Pessiot J.
AU - Truong T.
AU - Usunier N.
AU - Amini M.
AU - Gallinari P.
PY - 2007
SP - 145
EP - 151
DO - 10.5220/0002396301450151