Temporal Pattern-Aware QoS Prediction via Biased Non-Negative Latent Factorization of Tensors
Sign Up to like & getrecommendations! Published in 2020 at "IEEE Transactions on Cybernetics"
DOI: 10.1109/tcyb.2019.2903736
Abstract: Quality-of-service (QoS) data vary over time, making it vital to capture the temporal patterns hidden in such dynamic data for predicting missing ones with high accuracy. However, currently latent factor (LF) analysis-based QoS-predictors are mostly… read more here.
Keywords: biased non; factorization tensors; qos; negative latent ... See more keywords
Fast and Accurate Non-Negative Latent Factor Analysis of High-Dimensional and Sparse Matrices in Recommender Systems
Sign Up to like & getrecommendations! Published in 2023 at "IEEE Transactions on Knowledge and Data Engineering"
DOI: 10.1109/tkde.2021.3125252
Abstract: A fast non-negative latent factor (FNLF) model for a high-dimensional and sparse (HiDS) matrix adopts a Single Latent Factor-dependent, Non-negative, Multiplicative and Momentum-incorporated Update (SLF-NM2U) algorithm, which enables its fast convergence. It is crucial to… read more here.
Keywords: non negative; analysis; high dimensional; latent factor ... See more keywords
Assimilating Second-Order Information for Building Non-Negative Latent Factor Analysis-Based Recommenders
Sign Up to like & getrecommendations! Published in 2022 at "IEEE Transactions on Systems, Man, and Cybernetics: Systems"
DOI: 10.1109/tsmc.2020.3002762
Abstract: A non-negative latent factor analysis (NLFA)-based recommender can make precise recommendations by correctly representing the non-negative characteristic of industrial data. It commonly relies on a nonconvex and bilinear optimization process, where the effects of first-order… read more here.
Keywords: factor analysis; negative latent; non negative; second order ... See more keywords