Maximum Margin Matrix Factorization (MMMF) is an effective collaborative filtering approach. MMMF suffers from the data sparsity problem, i.e., the number of items rated by the users are very small as compared t
matrix U U VX . X V Lemma 3 can be used in order to formulate minimizing the trace norm as a semi-de?nite optimization problem (SDP). Soft-margin matrix factorization (1), can be written as: 1 min (tr A + tr B) + c 2 2 ξia s.t. ia∈S A X X B 0, yia Xia ≥ 1 ...
Fast maximum margin matrix factorization for collaborative prediction Maximum Margin Matrix Factorization (MMMF) was recently suggested (Srebro et al., 2005) as a convex, infinite dimensional alternative to low-rank approxima... JDM Rennie,N Srebro - Machine Learning, Twenty-second International ...
Maximum-Margin Matrix Factorization - NIPS Procee:最大间距矩阵分解-咬吧,Maximum-Margin Matrix Factorization - NIPS Procee:..
On the other hand, the NIMLC method could perfectly solve the Moons dataset and had by a significant margin the best clustering performance on Rings, which is the most difficult of the three synthetic datasets. It should be stressed that the NIMLC method presents the unique capability of ...
Maximum Margin Matrix Factorization (MMMF) has been a successful learning method in collaborative filtering research. For a partially observed ordinal rating matrix, the focus is on determining low-norm latent factor matrices U (of users) and V (of items) so as to simultaneously approximate the ...
I. Rish and G. Tesauro. Active collaborative prediction with maximum margin matrix factorization. Inform. Theory and App. Workshop, 2007.I. Rish and G. Tesauro, "Active collaborative prediction with maximum margin matrix factorization," in Information Theory and Applications Workshop, 2007....
maximum margin matrix factorizationrecommendationsocial mediaUser groups on photo sharing websites, such as Flickr, are self-organized communities to share photos and conversations with similar interest and have gained massive popularity. However, the huge volume of groups brings troubles for users to ...
To cope with this problem, one-class Maximum Margin Matrix Factorization (one-class MMMF), which inherits the merits of both the applicability of one-class SVM and the discriminative power of maximum margin matrix factorization, is proposed. Extensive experiments conducted on both simulated toy data...
Maximum Margin Matrix Factorization is one of the very popular techniques of collaborative filtering. The discrete valued rating matrix with a small portion of known ratings is factorized into two latdoi:10.1007/978-3-319-42911-3_14K. H. Salman...