Data sparsity is a major problem for collaborative filtering (CF) techniques in recommender systems, especially for new users and items. We observe that, while our target data are sparse for CF systems, related and relatively dense auxiliary data may already exist in some other more mature applicati... || cross domain; learn and transfer the latent vectors; principled matrix; heterogeneous； intrinsic preference structure; Y_{u:}行,Y_{:i}列，y_{ui}实体; 将ratings 处理成implicit feedback； coordinate systems; || Weike Pan, Evan W. Xiang, Nathan N. Liu and Qiang Yang...
Nonnegative matrix factorization (NMF) is a popular method for low-rank approximation of nonnegative matrix, pro- viding a useful tool for representation learning that is valuable for clustering and classification. When a portion of data are la- beled, the performance of clustering or classification... || semi-supervised NMF； 分解方法求解NMF; || Hyekyoung Lee, Jiho Yoo, and Seungjin Choi...
Given two homogeneous rating matrices with some overlapped users/items whose mappings are unknown, this paper aims at an- swering two questions. First, can we identify the unknown map- ping between the users and/or items? Second, can we further utilize the identified mappings to improve the quality ... || Collaborative Filtering; Transfer Learning; Matrix Factorization; cross domain； constraint;奇异值分解；CBT；RMGM; || Chung-Yi Li...
The sparsity problem in collaborative filtering (CF) is a major bottleneck for most CF methods. In this paper, we consider a novel approach for alleviat- ing the sparsity problem in CF by transferring user- item rating patterns from a dense auxiliary rating matrix in other domains (e.g., a popular m... || cross-domain; transfer；codebook；CBT; || Bin Li；Qiang Yang; Xiangyang Xue...
People often use multiple platforms to fulfill their different information needs. With the ultimate goal of serving people intelligently, a fundamental way is to get comprehensive understanding about user needs. How to organically integrate and bridge cross-platform information in a human-centric wa... || cross-domain; overlapped; transfer；semi- supervised NMF; || Meng Jiang, Peng Cui...
Nonnegative matrix factorization (NMF) can be formulated as a mini- mization problem with bound constraints. Although bound-constrained optimization has been studied extensively in both theory and practice, so far no study has formally applied its techniques to NMF. In this letter, we propose two pr... || 非负矩阵求法；NMF解释; VQ (vector quantization->K-means); || Chih-Jen Lin
Currently, most research on nonnegative matrix factorization (NMF) focus on 2-factor X = FG T factorization. We provide a systematic analysis of 3-factor X = FSG T NMF. While unconstrained 3-factor NMF is equivalent to unconstrained 2-factor NMF, constrained 3- factor NMF brings new features to cons... || 3-factor NMF; semi-NMF; 带有正交约束的NMF求解方法；证明NMF等同于k-means； Nonnegative, non-negative; trace, 矩阵的迹trace；矩阵分解自由度； the degree of freedom; || Chris Ding...
Non-negative matrix factorization (NMF) is a recently developed technique for finding parts-based, linear representations of non-negative data. Although it has successfully been applied in several applications, it does not always result in parts-based representations. In this paper, we show how expl... || non-negative matrix factorization, sparseness, data-adaptive representations；稀疏矩阵分解；; || Patrik O. Hoyer...
Traditional recommendation systems (RecSys) suffer from two problems: the exploitation-exploration dilemma and the cold-start problem. One solution to solving the exploitation- exploration dilemma is the contextual bandit policy, which adaptively exploits and explores user interests. As a result, th... || bandit policy; 线性 ；linear；exploitation; exploration; || Bo Liu, Ying Wei, Yu Zhang, Zhixian Yan, Qiang Yang...
