Advances in collaborative filtering
Collaborative filtering (CF) methods produce recommendations based on usage patterns
without the need of exogenous information about items or users. CF algorithms have shown …
without the need of exogenous information about items or users. CF algorithms have shown …
Position-transitional particle swarm optimization-incorporated latent factor analysis
High-dimensional and sparse (HiDS) matrices are frequently found in various industrial
applications. A latent factor analysis (LFA) model is commonly adopted to extract useful …
applications. A latent factor analysis (LFA) model is commonly adopted to extract useful …
Hierarchical particle swarm optimization-incorporated latent factor analysis for large-scale incomplete matrices
A Stochastic Gradient Descent (SGD)-based Latent Factor Analysis (LFA) model is highly
efficient in representative learning on a High-Dimensional and Sparse (HiDS) matrix, where …
efficient in representative learning on a High-Dimensional and Sparse (HiDS) matrix, where …
On the difficulty of evaluating baselines: A study on recommender systems
Numerical evaluations with comparisons to baselines play a central role when judging
research in recommender systems. In this paper, we show that running baselines properly is …
research in recommender systems. In this paper, we show that running baselines properly is …
Fast adaptively weighted matrix factorization for recommendation with implicit feedback
Recommendation from implicit feedback is a highly challenging task due to the lack of the
reliable observed negative data. A popular and effective approach for implicit …
reliable observed negative data. A popular and effective approach for implicit …
The datasets dilemma: How much do we really know about recommendation datasets?
There has been sustained interest from both academia and industry throughout the years
due to the importance and practicability of recommendation systems. However, several …
due to the importance and practicability of recommendation systems. However, several …
A differential evolution-enhanced position-transitional approach to latent factor analysis
High-dimensional and sparse (HiDS) matrices are frequently adopted to describe the
complex relationships in various big data-related systems and applications. A Position …
complex relationships in various big data-related systems and applications. A Position …
Samwalker: Social recommendation with informative sampling strategy
Recommendation from implicit feedback is a highly challenging task due to the lack of
reliable negative feedback data. Only positive feedback are observed and the unobserved …
reliable negative feedback data. Only positive feedback are observed and the unobserved …
Parallel fractional stochastic gradient descent with adaptive learning for recommender systems
The structural change toward the digital transformation of online sales elevates the
importance of parallel processing techniques in recommender systems, particularly in the …
importance of parallel processing techniques in recommender systems, particularly in the …
An adaptive latent factor model via particle swarm optimization
Q Wang, S Chen, X Luo - Neurocomputing, 2019 - Elsevier
Latent factor (LF) models are greatly efficient in extracting valuable knowledge from High-
Dimensional and Sparse (HiDS) matrices which are usually seen in many industrial …
Dimensional and Sparse (HiDS) matrices which are usually seen in many industrial …