Part of Advances in Neural Information Processing Systems 20 (NIPS 2007)
Markus Weimer, Alexandros Karatzoglou, Quoc Le, Alex Smola
In this paper, we consider collaborative ﬁltering as a ranking problem. We present a method which uses Maximum Margin Matrix Factorization and optimizes rank- ing instead of rating. We employ structured output prediction to optimize directly for ranking scores. Experimental results show that our method gives very good ranking scores and scales well on collaborative ﬁltering tasks.