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| - **Explore :** | - **Explore :** | ||
| - **Probabilistic Matrix Factorization (PMF)** (NIPS, 2008)[[http://web.mit.edu/~rsalakhu/www/papers/nips07_pmf.pdf|(pdf)]]------ [[http://www.wikicoursenote.com/wiki/Probabilistic_Matrix_Factorization|Introduction]] | - **Probabilistic Matrix Factorization (PMF)** (NIPS, 2008)[[http://web.mit.edu/~rsalakhu/www/papers/nips07_pmf.pdf|(pdf)]]------ [[http://www.wikicoursenote.com/wiki/Probabilistic_Matrix_Factorization|Introduction]] | ||
| - | - **Abstract:** Model collaborative filtering task as the classification or regression problem in machine learning and Apply SVD to reduce the dimensionality. | ||
| - | - **Explore :** | ||
| - **Abstract:** PMF apply a probabilistic approach using Gaussian assumptions on the knonw data and the factor matrics to factor the matrix and pridicting the missing values.Experimental resuts show that PMF perform quite well. | - **Abstract:** PMF apply a probabilistic approach using Gaussian assumptions on the knonw data and the factor matrics to factor the matrix and pridicting the missing values.Experimental resuts show that PMF perform quite well. | ||
| - **Superiority:** Scales linearly, performs well on the large, spase and imbalanced dataset. | - **Superiority:** Scales linearly, performs well on the large, spase and imbalanced dataset. | ||
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| ===== works ===== | ===== works ===== | ||
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| + | list of some papers: | ||
| + | |||
| + | 1. Relational learning via collective matrix Factorization : Ajit P.Singh | ||
| + | 2. Locality Preserving Nonnegative matrix factorization dengcai | ||
| + | 3. relation regularized matrix factorization, wu0jun Li | ||
| + | 4. Modeling user rating Profiles for collaborative filtering | ||
| + | 5. Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions | ||
| + | 6. application of dimensionality reduction in recommender system-a case study | ||
| + | 7. collaborative ifltering via guassian probabilistic latent semantic analysis | ||
| + | 8 item based collaborative filtering recommendation algorithms | ||
| + | 9. maximum likelihood estimation of intrinsic dimension | ||
| + | 10 Optimization algorithms in machine learning --- stephen wright | ||
| + | 11. global analytic solution for variational bayesian matrix factorization | ||
| + | 12. variational bayesian approach to movie rating prediction | ||
| + | 13. implicit regularization in variational bayesian matrix factorization | ||
| + | 14. sparse inverse covariance estimation with the graphical lasso | ||
| + | 15. matrix factorization techniques for recommender systemns | ||
| + | 16probabilistic sparse matrix factorization | ||
| + | 17. learning with local and global consistency | ||
| + | 18 | ||
