The latent factorization of tensors (LFT) develops an efficient way to fulfill the prediction for dynamic quality of service (QoS) data.
Jun 25, 2024 · The latent factorization of tensors (LFT) develops an efficient way to fulfill the prediction for dynamic quality of service (QoS) data and ...
Biased collective latent factorization of tensors with transfer learning for dynamic QoS data predicting. https://doi.org/10.1016/j.dsp.2023.104360.
Aug 1, 2024 · Jiaying Dong, Yan Song , Ming Li, Hong Rao: Biased collective latent factorization of tensors with transfer learning for dynamic QoS data ...
Jul 29, 2024 · Latent factorization of tensors (LFT) is an extremely efficient and typical approach for extracting such patterns from an HDI tensor. However, ...
Missing: Biased collective transfer
Empirical studies on two dynamic industrial QoS datasets show that compared with an SGD-based LFT model, an MLFT model achieves faster convergence rate and ...
Aug 29, 2024 · A Canonical Polyadic (CP)-based tensor network model has proven to be efficient for predicting dynamic QoS data. However, current CP-based ...
In cross-domain recommendation, data sparsity becomes more and more serious when the ratings are expressed numerically, e.g., 5-star grades.
This paper proposes an L 1 -and-L 2 -Regularized Nonnegative Tensor Factorization (LNTF) model to impute PLM missing data.
For dynamic QoS data in web services, Wu et al. propose six regularized Non-negative Latent Factorization of Tensors models, which are integrated to describe ...