Biased collective latent factorization of tensors with transfer learning for dynamic QoS data predicting

Author:

Dong Jiaying,Song YanORCID,Li Ming,Rao Hong

Funder

Natural Science Foundation of Shanghai Municipality

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Signal Processing,Artificial Intelligence,Applied Mathematics,Computer Vision and Pattern Recognition,Statistics, Probability and Uncertainty,Computational Theory and Mathematics

Reference57 articles.

1. Probabilistic approach for qos-aware recommender system for trustworthy web service selection;Mehdi;Appl. Intell.,2014

2. A graph-based qos prediction approach for web service recommendation;Chang;Appl. Intell.,2021

3. A double-space and double-norm ensembled latent factor model for highly accurate web service qos prediction;Wu;IEEE Trans. Serv. Comput.,2023

4. Neulft: a novel approach to nonlinear canonical polyadic decomposition on high-dimensional incomplete tensors;Luo;IEEE Trans. Knowl. Data Eng.,2023

5. A pid-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis;Wu;IEEE/CAA J. Autom. Sin.,2022

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