Discrete Martrix Factorization with Cramer Risk Minimization

Jianbo Ye (c) 2014-2015

This is an experimental code. Input

• A sparse matrix with values taking discrete values from 1 .. M, each row is a user, and each column is an item.
• A representation dimension for user and item factors (default: 10)

Output:

• A probability matrix for each cell (i,j) with a nonzero probability for discrete value from 0 ... (M+1), where 0 and (M+1) are considering as the "extrame values". One can either use the expected value as predictions or rank each row using extrame values.

Reference

• Dataset: http://grouplens.org/datasets/movielens/