The DOI matches its Crossref record; 15 of the 15 references it cites were found in a registry or an index.
Fast maximum margin matrix factorization for collaborative prediction
Abstract
Maximum Margin Matrix Factorization (MMMF) was recently suggested (Srebro et al., 2005) as a convex, infinite dimensional alternative to low-rank approximations and standard factor models. MMMF can be formulated as a semi-definite programming (SDP) and learned using standard SDP solvers. However, current SDP solvers can only handle MMMF problems on matrices of dimensionality up to a few hundred. Here, we investigate a direct gradient-based optimization method for MMMF and demonstrate it on large collaborative prediction problems. We compare against results obtained by Marlin (2004) and find that MMMF substantially outperforms all nine methods he tested.
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Cites
Learning the parts of objects by non-negative matrix factorization
Lee, D. D., & Seung, H. S. (1999) · Nature · doi:10.1038/44565 · Cited by 13,868
(2021) · A First Course in Statistical Programming with R · doi:10.1017/9781108993456.011 · Cited by 13,724
An Introduction to the Conjugate Gradient Method Without the Agonizing Pain
Shewchuk, J. R. (1994) · Cited by 2,173
Latent semantic models for collaborative filtering
Hofmann, T. (2004) · ACM Transactions on Information Systems · doi:10.1145/963770.963774 · Cited by 1,378
Learning Collaborative Information Filters
Billsus, D., & Pazzani, M. J. (1998) · Cited by 1,040
Maximum-Margin Matrix Factorization
Srebro, N., Rennie, J. D. M., & Jaakkola, T. (2004) · Cited by 962
Self-citationA rank minimization heuristic with application to minimum order system approximation
Fazel, M., Hindi, H., & Boyd, S. P. (2001) · Proceedings of the 2001 American Control Conference. (Cat. No.01CH37148) · doi:10.1109/acc.2001.945730 · Cited by 956
Weighted low-rank approximations
Srebro, N., & Jaakkola, T. (2003) · International Conference on Machine Learning · Cited by 715
Self-citationA Generalization of Principal Components Analysis to the Exponential Family
Collins, M., Dasgupta, S., & Schapire, R. E. (2001) · Cited by 382
Text Categorization Based on Regularized Linear Classification Methods
Zhang, T., & Oles, F. J. (2001) · Information Retrieval · doi:10.1023/a:1011441423217 · Cited by 379
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Exact Matrix Completion via Convex Optimization
Candès, E. J., & Recht, B. (2009) · Foundations of Computational Mathematics · doi:10.1007/s10208-009-9045-5 · Cited by 4,877
Introduction to Online Convex Optimization
Hazan, E. (2016) · Foundations and Trends in Optimization · doi:10.1561/2400000013 · Cited by 747
Canonical Tensor Decomposition for Knowledge Base Completion
Lacroix, T., Usunier, N., & Obozinski, G. (2018) · arXiv · doi:10.48550/arxiv.1806.07297 · Cited by 247
A New Approach to Collaborative Filtering: Operator Estimation with Spectral Regularization
Abernethy, J., Bach, F., Evgeniou, T., & Vert, J.-P. (2008) · arXiv · doi:10.48550/arxiv.0802.1430 · Cited by 210
Li, Z. (., Fang, X., & Sheng, O. R. L. (2018) · ACM Transactions on Management Information Systems · doi:10.1145/3131782 · Cited by 65
Metadata
- Authors
- Rennie, J. D. M., & Srebro, N.
- Venue
- Proceedings of the 22nd international conference on Machine learning - ICML '05
- Publisher
- ACM Press
- Published
- 2005
- Pages
- 713-719
- OpenAlex
- W1976618413
- Cited by
- 939
- References
- 15
- FWCI
- 13.85
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Rennie, J. D. M., & Srebro, N. (2005). Fast maximum margin matrix factorization for collaborative prediction. Proceedings of the 22nd international conference on Machine learning - ICML '05. https://doi.org/10.1145/1102351.1102441
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