A novel group recommendation model with two-stage deep learning
Group recommendation has recently drawn a lot of attention to the recommender system
community. Currently, several deep learning-based approaches are leveraged to learn …
community. Currently, several deep learning-based approaches are leveraged to learn …
A PID-incorporated latent factorization of tensors approach to dynamically weighted directed network analysis
A large-scale dynamically weighted directed network (DWDN) involving numerous entities
and massive dynamic interaction is an essential data source in many big-data-related …
and massive dynamic interaction is an essential data source in many big-data-related …
Scribble-supervised video object segmentation
P Huang, J Han, N Liu, J Ren… - IEEE/CAA Journal of …, 2021 - ieeexplore.ieee.org
Recently, video object segmentation has received great attention in the computer vision
community. Most of the existing methods heavily rely on the pixel-wise human annotations …
community. Most of the existing methods heavily rely on the pixel-wise human annotations …
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 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 survey of transformer-based multimodal pre-trained modals
With the broad industrialization of Artificial Intelligence (AI), we observe a large fraction of
real-world AI applications are multimodal in nature in terms of relevant data and ways of …
real-world AI applications are multimodal in nature in terms of relevant data and ways of …
A multilayered-and-randomized latent factor model for high-dimensional and sparse matrices
How to extract useful knowledge from a high-dimensional and sparse (HiDS) matrix
efficiently is critical for many big data-related applications. A latent factor (LF) model has …
efficiently is critical for many big data-related applications. A latent factor (LF) model has …
A PSO-based deep learning approach to classifying patients from emergency departments
In this paper, a deep belief network (DBN) is employed to deal with the problem of the
patient attendance disposal in accident & emergency (A&E) departments. The selection of …
patient attendance disposal in accident & emergency (A&E) departments. The selection of …
Position encoding based convolutional neural networks for machine remaining useful life prediction
Accurate remaining useful life (RUL) prediction is important in industrial systems. It prevents
machines from working under failure conditions, and ensures that the industrial system …
machines from working under failure conditions, and ensures that the industrial system …
DeepTSQP: Temporal-aware service QoS prediction via deep neural network and feature integration
Quality of service (QoS) has been mostly applied to represent non-functional properties of
web services and differentiate those with the same functionality. How to accurately predict …
web services and differentiate those with the same functionality. How to accurately predict …