The DOI matches its Crossref record; 20 of the 20 references it cites were found in a registry or an index.
Scalable Coordinate Descent Approaches to Parallel Matrix Factorization for Recommender Systems
Abstract
Matrix factorization, when the matrix has missing values, has become one of the leading techniques for recommender systems. To handle web-scale datasets with millions of users and billions of ratings, scalability becomes an important issue. Alternating Least Squares (ALS) and Stochastic Gradient Descent (SGD) are two popular approaches to compute matrix factorization. There has been a recent flurry of activity to parallelize these algorithms. However, due to the cubic time complexity in the target rank, ALS is not scalable to large-scale datasets. On the other hand, SGD conducts efficient updates but usually suffers from slow convergence that is sensitive to the parameters. Coordinate descent, a classical optimization approach, has been used for many other large-scale problems, but its application to matrix factorization for recommender systems has not been explored thoroughly. In this paper, we show that coordinate descent based methods have a more efficient update rule compared to ALS, and are faster and have more stable convergence than SGD. We study different update sequences and propose the CCD++ algorithm, which updatesrank-one factors one by one. In addition, CCD++ can be easily parallelized on both multi-core and distributed systems. We empirically show that CCD++ is much faster than ALS and SGD in both settings. As an example, on a synthetic dataset with 2 billion ratings, CCD++ is 4 times faster than both SGD and ALS using a distributed system with 20 machines.
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Cites
Bertsekas, D. P. (1997) · Journal of the Operational Research Society · doi:10.1038/sj.jors.2600425 · Cited by 11,726
Matrix Factorization Techniques for Recommender Systems
Koren, Y., Bell, R., & Volinsky, C. (2009) · Computer · doi:10.1109/mc.2009.263 · Cited by 11,673
Large-Scale Machine Learning with Stochastic Gradient Descent
Bottou, L. (2010) · Proceedings of COMPSTAT'2010 · doi:10.1007/978-3-7908-2604-3_16 · Cited by 5,795
Parlar, M. (2000) · Interactive Operations Research with Maple · doi:10.1007/978-1-4612-1356-7_5 · Cited by 1,741
Distributed GraphLab: a framework for machine learning and data mining in the cloud
Low, Y., Bickson, D., Gonzalez, J., Guestrin, C., Kyrola, A., & Hellerstein, J. M. (2012) · Proceedings of the VLDB Endowment · doi:10.14778/2212351.2212354 · Cited by 1,653
Hogwild: A Lock-Free Approach to Parallelizing Stochastic Gradient Descent
Niu, F., Recht, B., Re, C., & Wright, S. J. (2011) · arXiv · doi:10.48550/arxiv.1106.5730 · Cited by 1,211
Parallelized Stochastic Gradient Descent
Zinkevich, M., Weimer, M., Li, L., & Smola, A. J. (2010) · Cited by 1,124
A dual coordinate descent method for large-scale linear SVM
Hsieh, C.-J., Chang, K.-W., Lin, C.-J., Keerthi, S. S., & Sundararajan, S. (2008) · Proceedings of the 25th international conference on Machine learning - ICML '08 · doi:10.1145/1390156.1390208 · Cited by 872
Self-citationLarge-Scale Parallel Collaborative Filtering for the Netflix Prize
Zhou, Y., Wilkinson, D., Schreiber, R., & Pan, R. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-68880-8_32 · Cited by 700
Large-scale matrix factorization with distributed stochastic gradient descent
Gemulla, R., Nijkamp, E., Haas, P. J., & Sismanis, Y. (2011) · Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining · doi:10.1145/2020408.2020426 · Cited by 589
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Xing, E. P., Ho, Q., Dai, W., Kim, J. K., Wei, J., Lee, S., Zheng, X., Xie, P., Kumar, A., & Yu, Y. (2015) · IEEE Transactions on Big Data · doi:10.1109/tbdata.2015.2472014 · Cited by 388
Guaranteed Matrix Completion via Non-Convex Factorization
Sun, R., & Luo, Z.-Q. (2016) · IEEE Transactions on Information Theory · doi:10.1109/tit.2016.2598574 · Cited by 309
Evaluating Recommender Systems: Survey and Framework
Zangerle, E., & Bauer, C. (2023) · ACM Computing Surveys · doi:10.1145/3556536 · Cited by 283
GLMix: Generalized Linear Mixed Models For Large-Scale Response Prediction
Zhang, X., Zhou, Y., Ma, Y., Chen, B.-C., Zhang, L., & Agarwal, D. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939684 · Cited by 71
Metadata
- Authors
- Yu, H.-F., Hsieh, C.-J., Si, S., & Dhillon, I.
- Venue
- 2012 IEEE 12th International Conference on Data Mining
- Publisher
- IEEE
- Published
- 2012-12
- Pages
- 765-774
- ISSN
- 1550-4786 (ISSN-L)
- OpenAlex
- W2020098476
- Cited by
- 253
- References
- 20
- FWCI
- 13.64
- Type
- conference paper
- Language
- en
- Open access
- closed
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