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 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 …
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 …
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 …
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 …
An α–β-divergence-generalized recommender for highly accurate predictions of missing user preferences
To quantify user–item preferences, a recommender system (RS) commonly adopts a high-
dimensional and sparse (HiDS) matrix. Such a matrix can be represented by a non-negative …
dimensional and sparse (HiDS) matrix. Such a matrix can be represented by a non-negative …
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 …
An e-learning recommendation approach based on the self-organization of learning resource
S Wan, Z Niu - Knowledge-Based Systems, 2018 - Elsevier
In e-learning, most content-based (CB) recommender systems provide recommendations
depending on matching rules between learners and learning objects (LOs). Such learner …
depending on matching rules between learners and learning objects (LOs). Such learner …
Latent factor-based recommenders relying on extended stochastic gradient descent algorithms
High-dimensional and sparse (HiDS) matrices generated by recommender systems contain
rich knowledge regarding various desired patterns like users' potential preferences and …
rich knowledge regarding various desired patterns like users' potential preferences and …
Estimating and penalizing induced preference shifts in recommender systems
The content that a recommender system (RS) shows to users influences them. Therefore,
when choosing a recommender to deploy, one is implicitly also choosing to induce specific …
when choosing a recommender to deploy, one is implicitly also choosing to induce specific …