CoFiRank, Maximum Margin Matrix Factorization for Collaborative Ranking Export

@inproceedings{citeulike:3152592, abstract = {In this paper, we consider collaborative filtering as a ranking problem. We present a method which uses Maximum Margin Matrix Factorization and optimizes ranking 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.}, author = {Weimer, Markus and Karatzoglou, Alexandros and Le, Quoc V. and Smola, Alex}, booktitle = {Advances in Neural Information Processing Systems}, citeulike-article-id = {3152592}, comment = {Predict ranking instead of predict scores.}, keywords = {collaborative-filter, recommendation}, posted-at = {2008-08-25 03:49:56}, priority = {0}, title = {CoFiRank, Maximum Margin Matrix Factorization for Collaborative Ranking}, volume = {20}, year = {2007} }

TY - CONF ID - citeulike:3152592 L3 - citeulike-article-id:3152592 N2 - In this paper, we consider collaborative filtering as a ranking problem. We present a method which uses Maximum Margin Matrix Factorization and optimizes ranking 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. TI - CoFiRank, Maximum Margin Matrix Factorization for Collaborative Ranking VL - 20 T2 - Advances in Neural Information Processing Systems KW - collaborative-filter KW - recommendation N1 - Predict ranking instead of predict scores. AU - Weimer, Markus AU - Karatzoglou, Alexandros AU - Le, Quoc AU - Smola, Alex PY - 2007/// ER -