Variational autoencoder Bayesian matrix factorization (VABMF) for collaborative filtering

Electricity theft detection in incremental scenario: A novel semi-supervised approach based on hybrid replay strategy

R Yao, N Wang, W Ke, Z Liu, Z Yan… - IEEE Transactions on …, 2023 - ieeexplore.ieee.org
Deep learning (DL) has achieved great success in the field of electricity theft detection
(ETD). Most existing studies have used supervised mode to complete the DL-based ETD …
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Implicit regularization of a deep augmented neural network model for human motion prediction

GK Yadav, M Abdel-Nasser, HA Rashwan, D Puig… - Applied …, 2023 - Springer
Predicting human motion based on past observed motion is one of the challenging issues in
computer vision and graphics. Existing research works are dealing with this issue by using …
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[PDF] arxiv.org

Generative Modeling of Sparse Approximate Inverse Preconditioners

M Li, H Wang, PK Jimack - International Conference on Computational …, 2024 - Springer
We present a new deep learning paradigm for the generation of sparse approximate inverse
(SPAI) preconditioners for matrix systems arising from the mesh-based discretization of …
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NPGCL: neighbor enhancement and embedding perturbation with graph contrastive learning for recommendation

X Wu, H Wang, J Yao, Q Qian, J Song - Applied Intelligence, 2025 - Springer
Abstract Graph Neural Networks (GNNs) have significantly advanced recommendation
systems by modeling user-item interactions through bipartite graphs. However, real-world …
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Deep encoder–decoder-based shared learning for multi-criteria recommendation systems

S Fraihat, B Abu Tahon, B Alhijawi… - Neural Computing and …, 2023 - Springer
A recommendation system (RS) can help overcome information overload issues by offering
personalized predictions for users. Typically, RS considers the overall ratings of users on …
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Efficient QoS Data Prediction Based on Tensor Kernel Paradigm-Tensor Decomposition

H **a, J Xu, Q Dong, H Jia… - … Conference on Networking …, 2024 - ieeexplore.ieee.org
In the Mobile Edge Computing (MEC) environment, the prediction efficiency is low when
user recommendation is based on Quality of Service (QoS) data due to network environment …
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SNDAE: Self-Normalizing Deep AutoEncoder for Recommendation

N Idrissi, A Zellou, Z Bakkoury - International Conference On Big Data and …, 2022 - Springer
Users and consumers on the web are inundated with massive marketing and information
notices while they crave directly personalized and precise content from online businesses …
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