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 ...
Maximum-Margin Matrix Factorization - NIPS Procee:最大间距矩阵分解-咬吧,Maximum-Margin Matrix Factorization - NIPS Procee:..
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 ...
In this paper, we suggest an active sampling method based on the recently proposed Maximum-Margin Matrix Factorization (MMMF) [7], a linear factor model that was shown to outperform state-of-art collaborative prediction techniques. MMMF is formulated as a semi-definite program (SDP) that finds...
MatrixcompletionMatrixfactorizationMaximum 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...
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 ...
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...
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 (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 to the very large item space. Recently, techniques like cross-domain collaborative ...