Results 241 to 250 of about 129,793 (292)
In order to rationalise the magnetic properties of a newly synthesised Co2III$_{2}^{III}$Co6II$_{6}^{II}$ coordination cluster it was found essential to exploit the freedom to choose the local coordinate frames for describing the high‐spin CoII centres.
Joscha Nehrkorn +5 more
wiley +1 more source
Refining the role of selinexor in multiple myeloma: strategic use in a shifting treatment landscape. [PDF]
Gay F +39 more
europepmc +1 more source
Spatiotemporal Maps for Dynamic MRI Reconstruction. [PDF]
Lobos RA +8 more
europepmc +1 more source
Overcoming missing data in spatial metabolomics with machine learning imputation to accelerate downstream discovery. [PDF]
Feng T +8 more
europepmc +1 more source
Surgical Explant of Degenerated Transcatheter Valves: A New Type of Threat? [PDF]
Verolino G +6 more
europepmc +1 more source
Prognostic Role of the Endothelial Activation and Stress Index (EASIX) in Functional Outcomes and Mortality After Acute Ischemic Stroke: A Retrospective Pilot Cohort Study. [PDF]
Makris M +9 more
europepmc +1 more source
Impact of Basal Ganglia Perivascular Spaces on Ischemic and Hemorrhagic Risks in Patients Taking Antithrombotic Therapies. [PDF]
Iwamoto S +17 more
europepmc +1 more source
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ACM Transactions on Internet Technology, 2020
Recommendation systems have been widely used in large e-commerce websites, but cold start and data sparsity seriously affect the accuracy of recommendation. To solve these problems, we propose SSL-SVD, which works to mine the sparse trust between users and improve the performance of the recommendation system.
Zhengdi Hu +7 more
openaire +1 more source
Recommendation systems have been widely used in large e-commerce websites, but cold start and data sparsity seriously affect the accuracy of recommendation. To solve these problems, we propose SSL-SVD, which works to mine the sparse trust between users and improve the performance of the recommendation system.
Zhengdi Hu +7 more
openaire +1 more source
Proceedings of the 20th ACM SIGKDD international conference on Knowledge discovery and data mining, 2014
Singular Value Decomposition (SVD) is computationally costly and therefore a naive implementation does not scale to the needs of scenarios where data evolves continuously. While there are various on-line analysis and incremental decomposition techniques, these may not accurately represent the data or may be slow for the needs of many applications.
Xilun Chen, K. Selcuk Candan
openaire +1 more source
Singular Value Decomposition (SVD) is computationally costly and therefore a naive implementation does not scale to the needs of scenarios where data evolves continuously. While there are various on-line analysis and incremental decomposition techniques, these may not accurately represent the data or may be slow for the needs of many applications.
Xilun Chen, K. Selcuk Candan
openaire +1 more source

