nips nips2012 nips2012-75 nips2012-75-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Maksims Volkovs, Richard S. Zemel
Abstract: The primary application of collaborate filtering (CF) is to recommend a small set of items to a user, which entails ranking. Most approaches, however, formulate the CF problem as rating prediction, overlooking the ranking perspective. In this work we present a method for collaborative ranking that leverages the strengths of the two main CF approaches, neighborhood- and model-based. Our novel method is highly efficient, with only seventeen parameters to optimize and a single hyperparameter to tune, and beats the state-of-the-art collaborative ranking methods. We also show that parameters learned on datasets from one item domain yield excellent results on a dataset from very different item domain, without any retraining. 1
[1] The Yahoo! R1 dataset. http://webscope.sandbox.yahoo.com/catalog.php? datatype=r.
[2] S. Balakrishnan and S. Chopra. Collaborative ranking. In WSDM, 2012.
[3] J. Bennet and S. Lanning. The Netflix prize. www.cs.uic.edu/˜liub/ KDD-cup-2007/NetflixPrize-description.pdf.
[4] J. S. Breese, D. Heckerman, and C. Kadie. Empirical analysis of predictive algorithm for collaborative filtering. In UAI, 1998.
[5] C. J. C. Burges. From RankNet to LambdaRank to LambdaMART: An overview. Technical Report MSR-TR-2010-82, 2010.
[6] C. J. C. Burges, R. Rango, and Q. V. Le. Learning to rank with nonsmooth cost functions. In NIPS, 2007.
[7] O. Chapelle, Y. Chang, and T.-Y. Liu. The Yahoo! Learning To Rank Challenge. http: //learningtorankchallenge.yahoo.com, 2010.
[8] D. F. Gleich and L.-H. Lim. Rank aggregation via nuclear norm minimization. In SIGKDD, 2011.
[9] K. Y. Goldberg, T. Roeder, D. Gupta, and C. Perkins. Eigentaste: A constant time collaborative filtering algorithm. Information Retrieval, 4(2), 2001.
[10] J. Herlocker, J. A. Konstan, and J. Riedl. An empirical analysis of design choices in neighborhood-based collaborative filtering algorithms. Information Retrieval, 5(4), 2002.
[11] T. Hofmann. Latent semantic models for collaborative filtering. ACM Trans. Inf. Syst., 22(1), 2004.
[12] K. Jarvelin and J. Kekalainen. IR evaluation methods for retrieving highly relevant documents. In SIGIR, 2000.
[13] X. Jiang, L.-H. Lim, Y. Yao, and Y. Ye. Statistical ranking and combinatorial hodge theory. Mathematical Programming, 127, 2011.
[14] H. Li. Learning to Rank for Information Retrieval and Natural Language Processing. Morgan & Claypool, 2011.
[15] N. Liu and Q. Yang. Eigenrank: A ranking-oriented approach to collaborative filtering. In SIGIR, 2008.
[16] B. Marlin. Modeling user rating profiles for collaborative filtering. In NIPS, 2003.
[17] B. Marlin. Collaborative filtering: A machine learning perspective. Master’s thesis, University of Toronto, 2004.
[18] B. McFee, T. Bertin-Mahieux, D. Ellis, and G. R. G. Lanckriet. The Million Song Dataset Challenge. In WWW, http://www.kaggle.com/c/msdchallenge, 2012.
[19] D. M. Pennock, E. Horvitz, S. Lawrence, and C. L. Giles. Collaborative filtering by personality diagnosis: A hybrid memory and model-based approach. In UAI, 2000.
[20] P. Resnick, N. Iacovou, M. Suchak, P. Bergstrom, and J. Riedl. Grouplens: An open architecture for collaborative filtering of netnews. In CSCW, 1994.
[21] R. Salakhutdinov and A. Mnih. Probabilistic matrix factorization. In NIPS, 2008.
[22] B. Sarwar, G. Karypis, J. Konstan, and J. Riedl. Item-based collaborative filtering recommendation algorithms. In WWW, 2001.
[23] M. Weimer, A. Karatzoglou, Q. V. Le, and A. J. Smola. CofiRank - maximum margin matrix factorization for collaborative ranking. In NIPS, 2007.
[24] G.-R. Xue, C. Lin, Q. Yang, W. Xi, H.-J. Zeng, Y. Yu, and Z. Chen. Scalable collaborative filtering using cluster-based smoothing. In SIGIR, 2005. 9