… factorization is one of the effective methods to construct recommendation system. However,
… case with large-scale sparse data. In order to improve the recommendation accuracy while …

… 摘要(英) Recommender systems have become an essential … Collaborative filtering (CF), a
branch of recommender system, … a unified model called co-clustering with augmented matrices (…

… used algorithms in the recommendation system. With the … implicit semantic model is integrated
to fill the sparse matrix of user … A nonnegative latent fac⁃ tor model for large-scale sparse …

… matrix factorization, it is not applicable to handle large-scale … when factorizing a highly sparse
matrix with the rare existence … more complete SG solver for recommender systems, we will …

… [Objective] This paper proposes a matrix … in recommendation system. It tries to reduce
prediction errors, overcome the problem of data sparsity, and improve the robustness of matrix …

… for us to verify social theories on large-scale media data, eg, for … Non-negative constraints
are always applied to U and H, … evaluations: Consequences for recommender systems. In Proc. …

… , we shall propose a nonnegative matrix factorization based model, called SI-Model to unify
the … LSDH: A hashing approach for large-scale link prediction in microblogs. In Proc. the 28th …

… In this paper, we leverage large-scale unlabeled data for joint extraction of feature and
opinion words under a knowledge poor setting, in which only a few feature-opinion pairs are …

… TRBT) learning model for cross-domain recommendation. Firstly we extract latent factor and
… 信息,称 为GMTF(graph regularized weighted nonnegative matrix tri-factorization)模型.然而,…

… model's recommendation accuracy is superior to some of the most advanced recommendation
models,it can be applied to large-scale … Key Words recommend system,collaborative filter,…