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Fast and accurate non-negative latent factor analysis of high-dimensional and sparse matrices in recommender systems
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 …
matrix adopts a Single Latent Factor-dependent, Non-negative, Multiplicative and …
A Kalman-filter-incorporated latent factor analysis model for temporally dynamic sparse data
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 …
(QoS)-aware selection of Web services has become a hot yet thorny issue. Conducting …
A deep learning based trust-and tag-aware recommender system
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 …
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
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 …
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
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 …
dimensional and sparse (HiDS) matrices filled with non-negative data. Single latent factor …
Recommender system based on temporal models: a systematic review
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 …
enormous benefits in supporting users' needs through map** the available products to …
Recommender systems: a review
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 …
movies, music, or romantic partners, but they also are used to select which advertisements to …
An adaptive divergence-based non-negative latent factor model
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 …
non-negativity of interactions among numerous nodes, which is involved in countless …
Generalized nesterov's acceleration-incorporated, non-negative and adaptive latent factor analysis
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 …
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
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 …
patterns hidden in multichannel data emerging from various applications. It often adopts a …