PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Binary Principal Component Analysis in the Netflix Collaborative Filtering Task
Laszlo Kozma, Alexander Ilin and Tapani Raiko
In: MLSP 2009, 2-4 Sep 2009, Grenoble, France.


We propose an algorithm for binary principal component analysis (PCA) that scales well to very high dimensional and very sparse data. Binary PCA finds components from data assuming Bernoulli distributions for the observations. The probabilistic approach allows for straightforward treatment of missing values. An example application is collaborative filtering using the Netflix data. The results are comparable with those reported for single methods in the literature and through blending we are able to improve our previously obtained best result with PCA.

EPrint Type:Conference or Workshop Item (Oral)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Theory & Algorithms
ID Code:6410
Deposited By:Alexander Ilin
Deposited On:08 March 2010