  This paper describes the solution method taken by LeBuSiShu team for track1
in ACM KDD CUP 2011 contest (resulting in the 5th place). We identified two
main challenges: the unique item taxonomy characteristics as well as the large
data set size.To handle the item taxonomy, we present a novel method called
Matrix Factorization Item Taxonomy Regularization (MFITR). MFITR obtained the
2nd best prediction result out of more then ten implemented algorithms. For
rapidly computing multiple solutions of various algorithms, we have implemented
an open source parallel collaborative filtering library on top of the GraphLab
machine learning framework. We report some preliminary performance results
obtained using the BlackLight supercomputer.
