Maximum Margin Matrix Factorization (MMMF) was recently suggested (Srebro et al., 2005) as a convex, infinite dimensional alternative to low-rank approximations and standard facto...
We present a novel approach to collaborative prediction, using low-norm instead of low-rank factorizations. The approach is inspired by, and has strong connections to, large-margi...
Abstract. Collaborative filtering is a popular method for personalizing product recommendations. Maximum Margin Matrix Factorization (MMMF) has been proposed as one successful lear...
Markus Weimer, Alexandros Karatzoglou, Alex J. Smo...
With the sheer growth of online user data, it becomes challenging to develop preference learning algorithms that are sufficiently flexible in modeling but also affordable in com...
Kai Yu, Shenghuo Zhu, John D. Lafferty, Yihong Gon...
Nonnegative matrix factorization (NMF) is a widely-used method for low-rank approximation (LRA) of a nonnegative matrix (matrix with only nonnegative entries), where nonnegativity...