诺库维普

Projected Gradient Methods for Nonnegative Matrix Factorization 2018-07-04 16:53:53

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

Orthogonal Nonnegative Matrix Tri-factorizations for Clustering 2018-06-30 21:29:01

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...

Different Scheduling Algorithm in Cloud Computing: A Survey 2018-06-29 17:09:57

scheduling; survey; || International Journal of Modern Computer Science (IJMCS)...

Non-negative Matrix Factorization with Sparseness Constraints 2018-06-28 11:50:46

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...

Transferable Contextual Bandit for Cross-Domain Recommendation 2018-06-27 15:15:31

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...

Scheduling workflows with privacy protection constraints for big data applications on cloud 2018-06-26 09:26:47

privacy scheduling... || privacy, workflow scheduling; || Yiping Wen, Jinjun Chen...

Controlling Popularity Bias in Learning-to-Rank Recommendation 2018-06-19 09:14:33

Many recommendation algorithms suffer from popularity bias in their output: popular items are recommended frequently and less popular ones rarely, if at all. However, less popular, long-tail items are precisely those that are often desirable recommendations. In this paper, we introduce a flexible re... || Recommender systems; long-tail; Recommendation evaluation; Coverage; Learning to rank; 长尾；diversity；多样性; || Himan Abdollahpouri; Robin Burke; Bamshad Mobasher...

WWW 23

Austin, Texas, USA,

AAAI 23

Washington, DC, USA,

SIGIR2022

Madrid, Spain, July 11th to 15th, 2022

WSDM2023

TBD,

IJCAI2022

Austria, July 23-29, 2022