Low-Rank Matrix Factorization Algorithm

A Low-Rank Matrix Factorization Algorithm is a matrix factorization algorithm that can be implemented by a low-rank matrix factorization system (which solves a low-rank matrix factorization task intended to compress a large matrix into a low-rank matrices).

• See: SVD Algorithm, Bayesian Inference, MCMC Method, Bayesian Personalized Ranking Algorithm.

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References

2008

• (Salakhutdinov & Mnih, 2008) ⇒ Ruslan Salakhutdinov, and Andriy Mnih. (2008). “Bayesian probabilistic matrix factorization using Markov chain Monte Carlo.” In: Proceedings of the 25th International Conference on Machine learning (ICML 2008).
  • QUOTE: Low-rank matrix approximation methods provide one of the simplest and most effective approaches to collaborative filtering. Such models are usually fitted to data by finding a MAP estimate of the model parameters, a procedure that can be performed efficiently even on very large datasets.

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