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people:bo_xu:sorec [2010/12/01 21:21]
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people:bo_xu:sorec [2011/01/06 19:51] (current)
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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:
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+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