Introduction
James Bennett, Charles Elkan, Bing Liu, Padhraic Smyth, and Domonkos Tikk
The Netflix Prize
James Bennett and Stan Lanning
Improved Neighborhood-based Collaborative Filtering
Robert M. Bell and Yehuda Koren
Variational Bayesian Approach to Movie Rating Prediction
Yew Jin Lim and Yee Whye Teh
On the Gravity Recommendation System
Gabor Takacs, Istvan Pilaszy, Bottyan Nemeth, and Domonkos Tikk
Methods for Large Scale SVD with Missing Values
Miklos Kurucz, Andras A. Benczur, and Karoly Csalogany
Improving Regularized Singular Value Decomposition for Collaborative Filtering
Arkadiusz Paterek
Collaborative Filtering via Ensembles of Matrix Factorizations
Mingrui Wu
Who Rated What: a Combination of SVD, Correlation and Frequent Sequence Mining
Miklo Kurucz, Andras A. Benczur, Tamas Kiss, Istvan Nagy, Adrienn Szabo and Balazs Torma
A Classical predictive Modeling Approach for Task "Who Rated What" of the KDD Cup 2007
Jorge Sueiras, Alfonso Salafranca, and Jose Luis Florez
Predicting Who Rated What in Large-Scale Datasets
Yan Liu and Zhenzhen Kou
A Two-Phase Spectral Bigraph Co-Clustering Approach for the Who Rated What
Task in KDD Cup 2007
Ting Liu, Yonghong Tian, and Wen Gao
Making the Most of Your Data: KDD Cup 2007 "How Many Ratings" Winners Report
Saharon Rosset, Claudia Perlich, and Yan Liu
A Combination of Approaches to Solve Task "How Many Ratings" of the KDD Cup 2007
Jorge Sueiras, Daniel Velez, and Joe Luis Florez
KDD Cup 2007 - How often Will That Movie be Rated?
James Malaugh, Sachin Gangaputra, and Nikhil Rastogi
K-split Based Approach to Predict Movie Rating Frequency
Hariprasad Bommaganti and Anand Nagarajan