The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Learning from Incomplete Ratings Using Non-negative Matrix Factorization
Abstract
We use a low-dimensional linear model to describe the user rating matrix in a recommendation system. A non-negativity constraint is enforced in the linear model to ensure that each user's rating profile can be represented as an additive linear combination of canonical coordinates. In order to learn such a constrained linear model from an incomplete rating matrix, we introduce two variations on Non-negative Matrix Factorization (NMF): one based on the Expectation-Maximization (EM) procedure and the other a Weighted Nonnegative Matrix Factorization (WNMF). Based on our experiments, the EM procedure converges well empirically and is less susceptible to the initial starting conditions than WNMF, but the latter is much more computationally efficient. Taking into account the advantages of both algorithms, a hybrid approach is presented and shown to be effective in real data sets. Overall, the NMF-based algorithms obtain the best prediction performance compared with other popular collaborative filtering algorithms in our experiments; the resulting linear models also contain useful patterns and features corresponding to user communities.
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Cites
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Learning the parts of objects by non-negative matrix factorization
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Empirical Analysis of Predictive Algorithms for Collaborative Filtering
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An Algorithmic Framework for Performing Collaborative Filtering
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Cited by
Nonnegative Matrix Factorization: A Comprehensive Review
Wang, Y.-X., & Zhang, Y.-J. (2013) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2012.51 · Cited by 981
Luo, X., Zhou, M., Xia, Y., & Zhu, Q. (2014) · IEEE Transactions on Industrial Informatics · doi:10.1109/tii.2014.2308433 · Cited by 629
Luo, X., Zhou, M., Xia, Y., Zhu, Q., Ammari, A. C., & Alabdulwahab, A. (2016) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2015.2412037 · Cited by 260
Personalized Mobile App Recommendation: Reconciling App Functionality and User Privacy Preference
Liu, B., Kong, D., Cen, L., Gong, N. Z., Jin, H., & Xiong, H. (2015) · Proceedings of the Eighth ACM International Conference on Web Search and Data Mining · doi:10.1145/2684822.2685322 · Cited by 98
(2012) · doi:10.5555/2343576.2343586 · Cited by 64
Metadata
- Authors
- Zhang, S., Wang, W., Ford, J., & Makedon, F.
- Venue
- Proceedings of the 2006 SIAM International Conference on Data Mining
- Published
- 2006-04-20
- Pages
- 549-553
- OpenAlex
- W1479822238
- Cited by
- 374
- References
- 22
- FWCI
- 2.05
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Zhang, S., Wang, W., Ford, J., & Makedon, F. (2006). Learning from Incomplete Ratings Using Non-negative Matrix Factorization. Proceedings of the 2006 SIAM International Conference on Data Mining. https://doi.org/10.1137/1.9781611972764.58
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