Fast and accurate non-negative latent factor analysis of high-dimensional and sparse matrices in recommender systems

X Luo, Y Zhou, Z Liu, MC Zhou - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
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 …

A Kalman-filter-incorporated latent factor analysis model for temporally dynamic sparse data

Y Yuan, X Luo, M Shang, Z Wang - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
With the rapid development of services computing in the past decade, Quality-of-Service
(QoS)-aware selection of Web services has become a hot yet thorny issue. Conducting …

A deep learning based trust-and tag-aware recommender system

S Ahmadian, M Ahmadian, M Jalili - Neurocomputing, 2022 - Elsevier
Recommender systems are popular tools used in many applications, such as e-commerce, e-
learning, and social networks to help users select their desired items. Collaborative filtering …

Adaptively-accelerated parallel stochastic gradient descent for high-dimensional and incomplete data representation learning

W Qin, X Luo, MC Zhou - IEEE Transactions on Big Data, 2023 - ieeexplore.ieee.org
High-dimensional and incomplete (HDI) interactions among numerous nodes are commonly
encountered in a Big Data-related application, like user-item interactions in a recommender …

A fast non-negative latent factor model based on generalized momentum method

X Luo, Z Liu, S Li, M Shang… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
Non-negative latent factor (NLF) models can efficiently acquire useful knowledge from high-
dimensional and sparse (HiDS) matrices filled with non-negative data. Single latent factor …

Recommender system based on temporal models: a systematic review

I Rabiu, N Salim, A Da'u, A Osman - Applied Sciences, 2020 - mdpi.com
Over the years, the recommender systems (RS) have witnessed an increasing growth for its
enormous benefits in supporting users' needs through map** the available products to …

Recommender systems: a review

PM LeBlanc, D Banks, L Fu, M Li, Z Tang… - Journal of the American …, 2024 - Taylor & Francis
Recommender systems are the engine of online advertising. Not only do they suggest
movies, music, or romantic partners, but they also are used to select which advertisements to …

An adaptive divergence-based non-negative latent factor model

Y Yuan, R Wang, G Yuan, L **n - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
A High-dimensional and incomplete (HDI) matrix is regularly adopted to portray the inherent
non-negativity of interactions among numerous nodes, which is involved in countless …

Generalized nesterov's acceleration-incorporated, non-negative and adaptive latent factor analysis

X Luo, Y Zhou, Z Liu, L Hu… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
A non-negative latent factor (NLF) model with a single latent factor-dependent, non-negative
and multiplicative update (SLF-NMU) algorithm is frequently adopted to extract useful …

Adjusting learning depth in nonnegative latent factorization of tensors for accurately modeling temporal patterns in dynamic QoS data

X Luo, M Chen, H Wu, Z Liu, H Yuan… - IEEE Transactions on …, 2021 - ieeexplore.ieee.org
A nonnegative latent factorization of tensors (NLFT) model precisely represents the temporal
patterns hidden in multichannel data emerging from various applications. It often adopts a …