Collaborative ranking

WSDM 2012 - Proceedings of the 5th ACM International Conference on Web Search and Data Mining

Volumn , Issue , 2012, Pages 143-152

  Balakrishnan, Suhrid ^a   Chopra, Sumit ^a  

^a AT AND T LABS RESEARCH   (United States)

Author keywords

Collaborative ranking; Learning to rank; NDCG; Recommender systems; RMSE

Indexed keywords

COLLABORATIVE FILTERING; COLLABORATIVE RANKING; DATA SETS; EVALUATION METRICS; LEARNING TO RANK; MATRIX FACTORIZATIONS; NDCG; OPTIMAL CHOICE; RMSE; ROOT MEAN SQUARED ERRORS; WEB SEARCHES;

DATA MINING; RECOMMENDER SYSTEMS;

WEBSITES;

EID: 84858049349     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1145/2124295.2124314     Document Type: Conference Paper

References (24)

1. Y. Bengio, R. Ducharme, P. Vincent, and C. Janvin. A neural probabilistic language model. Journal of Machine Learning Research, 3:1137-1155, 2003.
2. L. Breiman. Random forests. In Machine Learning, volume 45(1), 2001.
3. C. Burges. From ranknet to lambdarank to lambdamart: An overview. In Microsoft Research Technical Report MSR-TR-2010-82, 2010.
4. R. Collobert and J. Weston. A unified architecture for natural language processing: deep neural networks with multitask learning. In ICML '08: Proceedings of the 25th international conference on Machine learning, pages 160-167, New York, NY, USA, 2008. ACM.
5. D. Cossock and T. Zhang. Statistical analysis of bayes optimal subset ranking. IEEE Transactions on Information Theory, 54(11):5140-5154, 2008.
6. P. Cremonesi, Y. Koren, and R. Turrin. Performance of recommender algorithms on top-n recommendation tasks. In Proceedings of the fourth ACM conference on Recommender systems, RecSys '10, pages 39-46, New York, NY, USA, 2010. ACM.
7. P. Donmez, K. M. Svore, and C. J. Burges. On the local optimality of lambdarank. In Proceedings of the 32nd international ACM SIGIR conference on Research and development in information retrieval, SIGIR '09, pages 460-467, New York, NY, USA, 2009. ACM.
8. Y. Freund, R. Iyer, R. E. Schapire, and Y. Singer. An efficient boosting algorithm for combining preferences. J. Mach. Learn. Res., 4:933-969, 2003.
9. J. H. Friedman. Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29:1189-1232, 2000. (Pubitemid 33405972)
10. S. Hacker and L. von Ahn. Matchin: eliciting user preferences with an online game. In CHI '09: Proceedings of the 27th international conference on Human factors in computing systems, pages 1207 -1216, New York, NY, USA, 2009. ACM.
11. R. Herbrich, T. Graepel, and K. Obermayer. Large margin rank boundaries for ordinal regression. In P. J. Bartlett, B. Schölkopf, D. Schuurmans, and A. J. Smola, editors, Advances in Large Margin Classifiers, pages 115-132. MIT Press, 2000.
12. K. Järvelin and J. Kekäläinen. Cumulated gain-based evaluation of ir techniques. ACM Trans. Inf. Syst., 20:422-446, October 2002. (Pubitemid 44642296)
13. T. Joachims. Optimizing search engines using clickthrough data. In Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, KDD '02, pages 133-142, New York, NY, USA, 2002. ACM.
14. Y. Koren, R. Bell, and C. Volinsky. Matrix Factorization Techniques for Recommender Systems. Computer, 42(8):30-37, 2009.
15. P. Li, C. J. C. Burges, and Q. Wu. Mcrank: Learning to rank using multiple classification and gradient boosting, 2007.
16. B. M. Marlin and R. S. Zemel. Collaborative prediction and ranking with non-random missing data. Proceedings of the third ACM conference on Recommender systems (RecSys '09), 2009.
17. P. Melville and V. Sindhwani. Recommender Systems. 2010.
18. A. Mohan, Z. Chen, and K. Q. Weinberger. Web-search ranking with initialized gradient boosted regression trees. Journal of Machine Learning Research, Workshop and Conference Proceedings, 14:77-89, 2011.
19. M. Richardson, E. Dominowska, and R. Ragno. Predicting clicks: estimating the click-through rate for new ads. In Proceedings of the 16th international conference on World Wide Web, WWW '07, pages 521-530, New York, NY, USA, 2007. ACM. (Pubitemid 47582281)
20. R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. In Neural Information Processing Systems (NIPS), 2007.
21. H. Steck and R. S. Zemel. A generalized probabilistic framework and its variants for training top-k recommender systems. Workshop on the Practical Use of Recommender Systems, Algorithms and Technologies (PRSAT 2010)in conjunction with RecSys 2010, 2010.
22. M. Weimer, A. Karatzoglou, Q. V. Le, and A. Smola. Cofi-rank: Maximum margin matrix factorization for collaborative ranking. In Neural Information Processing Systems (NIPS), 2007.
23. J. Xu and H. Li. Adarank: a boosting algorithm for information retrieval. In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '07, pages 391-398, New York, NY, USA, 2007. ACM. (Pubitemid 350164985)
24. J. Xu, T.-Y. Liu, M. Lu, H. Li, and W.-Y. Ma. Directly optimizing evaluation measures in learning to rank. In Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR '08, pages 107-114, New York, NY, USA, 2008. ACM.