Matrix Factorization Techniques for Recommender Systems
Computer2009Vol. 42(8), pp. 30–37
Citations Over TimeTop 1% of 2009 papers
Abstract
As the Netflix Prize competition has demonstrated, matrix factorization models are superior to classic nearest neighbor techniques for producing product recommendations, allowing the incorporation of additional information such as implicit feedback, temporal effects, and confidence levels.
Related Papers
- → Non-Negative Matrix Factorization with Constraints(2010)60 cited
- → CUR+NMF for learning spectral features from large data matrix(2008)10 cited
- → Sparsity promoted non-negative matrix factorization for source separation and detection(2014)3 cited
- → Detection of Brain Activity in Functional Magnetic Resonance Imaging Data using Matrix Factorization(2013)1 cited
- → PHASL-NMF: Hierarchical ALS Based Power Non-Negative Matrix Factorization(2023)