Collaborative Preference Learning

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Reference

Alexandros Karatzoglou, Markus Weimer: Collaborative Preference Learning. In: Fürnkranz, J. and Hüllermeier, E.: Preference Learning, 2011, 409-427.

DOI

http://dx.doi.org/10.1007/978-3-642-14125-6_19

Abstract

Every recommender system needs the notion of preferences of a user to suggest one item and not another. However, current recommender algorithms deduct these preferences by first predicting an actual rating of the items and then sorting those. Departing from this, we present an algorithm that is capable of directly learning the preference function from given ratings. The presented approach combines recent results on preference learning, state-of-the-art optimization algorithms, and the large margin approach to capacity control. The algorithm follows the matrix factorization paradigm to collaborative filtering. Maximum Margin Matrix Factorization (MMMF) has been introduced to control the capacity of the prediction to avoid overfitting. We present an extension to this approach that is capable of using the methodology developed by the Learning to Rank community to learn a ranking of unrated items for each user. In addition, we integrate several recently proposed extensions to MMMF into one coherent framework where they can be combined in a mix-and-match fashion.

Extended Abstract

Bibtex

@incollection{
year={2011},
isbn={978-3-642-14124-9},
booktitle={Preference Learning},
editor={Fürnkranz, Johannes and Hüllermeier, Eyke},
doi={10.1007/978-3-642-14125-6_19},
title={Collaborative Preference Learning},
url={http://dx.doi.org/10.1007/978-3-642-14125-6_19, http://de.evo-art.org/index.php?title=Collaborative_Preference_Learning },
publisher={Springer Berlin Heidelberg},
author={Karatzoglou, Alexandros and Weimer, Markus},
pages={409-427},
language={English}
}

Used References

1. J. Basilico, T. Hofmann, Unifying collaborative and content-based filtering, in Proceedings of the 21st International Conference on Machine Learning (ICML) (ACM, New York, NY, 2004), pp. 65–72

2. L. Bottou, Stochastic learning, in Advanced Lectures on Machine Learning, Lecture Notes in Artificial Intelligence, LNAI 3176, ed. by O. Bousquet, U. von Luxburg (Springer, Berlin, 2004), pp. 146–168

3. C.J. Burges, Q.V. Le, R. Ragno, Learning to rank with nonsmooth cost functions, in Advances in Neural Information Processing Systems (NIPS), vol. 19, ed. by B. Schölkopf, J. Platt, T. Hofmann (2007), pp. 193–200

4. O. Chapelle, Q.V. Le, A. Smola, Large margin optimization of ranking measures, in NIPS Workshop: Machine Learning for Web Search (2007)

5. W. Chu, Z. Ghahramani, Gaussian processes for ordinal regression. J. Mach. Learn. Res. 6, 1019–1041 (2005)MathSciNetMATH

6. R. Herbrich, T. Graepel, K. Obermayer, Large margin rank boundaries for ordinal regression, in Advances in Large Margin Classifiers, ed. by A.J. Smola, P.L. Bartlett, B. Schölkopf, D. Schuurmans (MIT, Cambridge, MA, 2000), pp. 115–132

7. T. Hofmann, Latent semantic models for collaborative filtering. ACM Trans. Inf. Syst. (TOIS) 22(1), 89–115 (2004)

8. T. Joachims, Training linear SVMs in linear time, in Proceedings of the ACM Conference on Knowledge Discovery and Data Mining (KDD) (ACM, 2006), pp. 217–226

9. J. Nocedal, S.J. Wright, Numerical Optimization, Springer Series in Operations Research (Springer, 1999)

10. T. Qin, T.-Y. Liu, H. Li, A general approximation framework for direct optimization of information retrieval measures. Technical Report MSR-TR-2008-164, Microsoft Research, November 2008

11. J. Rennie, N. Srebro, Fast maximum margin matrix factoriazation for collaborative prediction, in Proceedings of the 22nd International Conference on Machine Learning (ICML) (2005), pp. 713–719

12. R. Salakhutdinov, A. Mnih, Probabilistic matrix factorization, in Advances in Neural Information Processing Systems (NIPS), vol. 20 (MIT, Cambridge, MA, 2008)

13. A. Smola, S.V.N. Vishwanathan, Q. Le, Bundle methods for machine learning, in Advances in Neural Information Processing Systems (NIPS), vol. 20 (MIT, Cambridge, MA, 2008)

14. A.J. Smola, I.R. Kondor, Kernels and regularization on graphs, in Proceedings of the Annual Conference on Computational Learning Theory (COLT), Lecture Notes in Computer Science, ed. by B. Schölkopf, M.K. Warmuth (Springer, Heidelberg, Germany, 2003), pp. 144–158

15. N. Srebro, T. Jaakkola, Weighted low-rank approximations, in Proceedings of the 20th International Conference on Machine Learning (ICML 2003) (AAAI, 2003), pp. 720–727

16. N. Srebro, J. Rennie, T. Jaakkola, Maximum-margin matrix factorization, in Advances in Neural Information Processing Systems (NIPS), vol. 17, ed. by L.K. Saul, Y. Weiss, L. Bottou (MIT, Cambridge, MA, 2005), pp. 1329–1336

17. N. Srebro, A. Shraibman, Rank, trace-norm and max-norm, in Proceedings of the Annual Conference on Computational Learning Theory (COLT), vol. 3559, Lecture Notes in Artificial Intelligence, ed. by P. Auer, R. Meir (Springer, 2005), pp. 545–560

18. G. Takács, I. Pilászy, B. Németh, D. Tikk, Major components of the gravity recommendation system. SIGKDD Explor. Newslett. 9(2), 80–83 (2007) http://dx.doi.org/10.1145/1345448.1345466

19. B. Taskar, C. Guestrin, D. Koller, Max-margin Markov networks, in Advances in Neural Information Processing Systems (NIPS), vol. 16, ed. by S. Thrun, L. Saul, B. Schölkopf (MIT, Cambridge, MA, 2004), pp. 25–32

20. I. Tsochantaridis, T. Joachims, T. Hofmann, Y. Altun, Large margin methods for structured and interdependent output variables. J. Mach. Learn. Res. 6, 1453–1484 (2005)MathSciNetMATH

21. M. Weimer, A. Karatzoglou, Q. Le, A. Smola, Cofirank - maximum margin matrix factorization for collaborative ranking, in Advances in Neural Information Processing Systems (NIPS) vol. 20 (MIT, Cambridge, MA, 2008)

22. M. Weimer, A. Karatzoglou, A. Smola, Adaptive collaborative filtering, in Proceedings of ACM Recommender Systems 2008 (2008), pp. 275–282

23. M. Weimer, A. Karatzoglou, A. Smola, Improving maximum margin matrix factorization. Mach. Learn. 72(3), 263–276 (2008) http://dx.doi.org/10.1007/s10994-008-5073-7

24. M. Weimer, A. Karatzoglou, A. Smola, Improving maximum margin matrix factorization, in Machine Learning and Knowledge Discovery in Databases, vol. 5211, LNAI, ed. by W. Daelemans, B. Goethals, K. Morik (Springer, 2008), pp. 14–14

25. J. Yu, S.V.N. Vishwanathan, S. Günter, N.N. Schraudolph, A quasi-Newton approach to nonsmooth convex optimization, In Proceedings of the 25th International Conference on Machine Learning (ICML), ed. by A. McCallum, S. Roweis (Omnipress, 2008.), pp. 1216–1223

26. S. Yu, K. Yu, V. Tresp, H.P. Kriegel, Collaborative ordinal regression, in Proceedings of the 23rd International Conference on Machine Learning (ICML), ed. by W.W. Cohen, A. Moore (ACM, 2006), pp. 1089–1096


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