Session-based recommendation with hypergraph convolutional networks and sequential information embeddings
Session-based recommendation focuses on predicting the next item that an anonymous
user is most likely to click. Due to its privacy-protecting ability, it is receiving increasing …
user is most likely to click. Due to its privacy-protecting ability, it is receiving increasing …
Deep neural network-based multi-stakeholder recommendation system exploiting multi-criteria ratings for preference learning
A commercially viable multi-stakeholder recommendation system maximizes the utility gain
by learning the personalized preferences of multiple stakeholders, such as consumers and …
by learning the personalized preferences of multiple stakeholders, such as consumers and …
Fusing logic rule-based hybrid variable graph neural network approaches to fault diagnosis of industrial processes
M Yin, J Li, Y Shi, C Qi, H Li - Expert Systems with Applications, 2024 - Elsevier
It is practical that industrial processes are regarded as hybrid systems involving both
continuous and discrete variables. Particularly, process discrete variables are usually rather …
continuous and discrete variables. Particularly, process discrete variables are usually rather …
Noise-reducing graph neural network with intent-target co-action for session-based recommendation
Session-based recommendation (SBR) originates from a real-world need to provide
effective recommendation solutions for unlogged users. How to utilize short interaction …
effective recommendation solutions for unlogged users. How to utilize short interaction …
A multi-behavior recommendation method exploring the preference differences among various behaviors
M Gan, G Xu, Y Ma - Expert Systems with Applications, 2023 - Elsevier
User behavior data has been widely used in recent research of recommendation systems.
Existing work usually utilize only single behavior instead of multi-behavior. However, there …
Existing work usually utilize only single behavior instead of multi-behavior. However, there …
Neighbor enhanced contextual graph neural network for session-based recommendation
Z Yang, M Yan, Y Yang, D Wang - Multimedia Tools and Applications, 2024 - Springer
Session-based recommender system (SBRS) has received increasingly extensive attention
in many fields, predicting whether a user will click on the next item according to the session …
in many fields, predicting whether a user will click on the next item according to the session …
GTPAN: Global Target Preference Attention Network for session-based recommendation
T Lu, X Xiao, Y Xiao, J Wen - Expert Systems with Applications, 2024 - Elsevier
Session-based recommendations (SBR) aim to predict the user's next choice of items based
on historical data. It is hard to access the interaction information between users and we can …
on historical data. It is hard to access the interaction information between users and we can …
Global heterogeneous graph enhanced category-aware attention network for session-based recommendation
Session-based recommendation (SBR) aims at predicting the next item based on an
ongoing recorded session of user's behaviors. Most of existing approaches mainly focus on …
ongoing recorded session of user's behaviors. Most of existing approaches mainly focus on …
A dual fusion deep convolutional network for blind universal image denoising
Z Lyu, Y Chen, H Sun, Y Hou - Signal Processing: Image Communication, 2024 - Elsevier
Blind image denoising and edge-preserving are two primary challenges to recover an image
from low-level vision to high-level vision. Blind denoising requires a single denoiser can …
from low-level vision to high-level vision. Blind denoising requires a single denoiser can …
EMARec: a sequential recommendation with exponential moving average
R Chen, Z Wang, C Tang, J Zhang, P Li, X Kong… - Neural Computing and …, 2024 - Springer
Capturing dynamic preference features from user historical behavioral data is widely applied
to improve the accuracy of recommendations in sequential recommendation tasks. However …
to improve the accuracy of recommendations in sequential recommendation tasks. However …