X Luo, Y Yuan, S Chen, N Zeng… - IEEE Transactions on …, 2020 - ieeexplore.ieee.org
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 …
J Chen, X Luo, M Zhou - IEEE Transactions on Big Data, 2021 - ieeexplore.ieee.org
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 …
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 …
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 …
JY Chin, Y Chen, G Cong - … Conference on Web Search and Data …, 2022 - dl.acm.org
There has been sustained interest from both academia and industry throughout the years due to the importance and practicability of recommendation systems. However, several …
J Chen, R Wang, D Wu, X Luo - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
High-dimensional and sparse (HiDS) matrices are frequently adopted to describe the complex relationships in various big data-related systems and applications. A Position …
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 …
The structural change toward the digital transformation of online sales elevates the importance of parallel processing techniques in recommender systems, particularly in the …
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 …