Suggested Papers/Readings

Collaborative Filtering

R. Bell and Y. Koren. Scalable Collaborative Filtering with Jointly Derived Neighborhood Interpolation Weights. International Conference on Data Mining (ICDM), 2007^*.
A. Buchanan and A.W. Fitzgibbon. Damped Newton algorithms for matrix factorization with missing data. International Conference on Computer Vision and Pattern Recognition (CVPR), 2005^*.

Manifold Learning

A. Singer. A Remark on Global Positioning from Local Distances. Submitted^*.
J. Tenenbaum, V. De Silva, and J. C. Langford. A global geometric framework for non-linear dimensionality reduction. Science, 2000^*.

Non-negative Matrix Approximation

D. D. Lee and H. S. Seung. Algorithms for Non-negative Matrix Factorization. Advances in Neural Information Processing Systems (NIPS), 2001^*.
D. D. Lee and H. S. Seung. Learning the parts of objects by non-negative matrix factorization. Nature, 1999^*.
Patrik O. Hoyer.Non-negative Matrix Factorization with Sparseness Constraints. JMLR, 2004.

Support Vector Machines

R. C. Bunescu and R. J. Mooney. Multiple Instance Learning for sparse positive bags. ICML, 2007^*.
SVM Regression. Section 7.1.4, Pattern Recognition and Machine Learning by Christopher M. Bishop (Course textbook)^*.

Web Search

Brin, S., Motwani, R., Page, L., and Winograd, T. What can you do with a web in your pocket?. IEEE Bulle. Techn. Comm. Data Eng., 1998.

Random Graph Models

Jure Leskovec and Christos Faloutsos. Scalable Modeling of Real Graphs using Kronecker Multiplications. ICML, 2007.

Graph Clustering

Inderjit S. Dhillon, Yuqiang Guan, and Brian Kulis. Weighted Graph Cuts without Eigenvectors: A Multilevel Approach. IEEE Trans. PAMI, 2007^*.

^*Already presented or have been selected for presentation.