A survey of recommendation systems: recommendation models, techniques, and application fields
This paper reviews the research trends that link the advanced technical aspects of
recommendation systems that are used in various service areas and the business aspects of …
recommendation systems that are used in various service areas and the business aspects of …
Graph neural networks: foundation, frontiers and applications
The field of graph neural networks (GNNs) has seen rapid and incredible strides over the
recent years. Graph neural networks, also known as deep learning on graphs, graph …
recent years. Graph neural networks, also known as deep learning on graphs, graph …
Contrastive meta learning with behavior multiplicity for recommendation
A well-informed recommendation framework could not only help users identify their
interested items, but also benefit the revenue of various online platforms (eg, e-commerce …
interested items, but also benefit the revenue of various online platforms (eg, e-commerce …
Gppt: Graph pre-training and prompt tuning to generalize graph neural networks
Despite the promising representation learning of graph neural networks (GNNs), the
supervised training of GNNs notoriously requires large amounts of labeled data from each …
supervised training of GNNs notoriously requires large amounts of labeled data from each …
Recommender systems based on graph embedding techniques: A review
Y Deng - IEEE Access, 2022 - ieeexplore.ieee.org
As a pivotal tool to alleviate the information overload problem, recommender systems aim to
predict user's preferred items from millions of candidates by analyzing observed user-item …
predict user's preferred items from millions of candidates by analyzing observed user-item …
Bars: Towards open benchmarking for recommender systems
The past two decades have witnessed the rapid development of personalized
recommendation techniques. Despite the significant progress made in both research and …
recommendation techniques. Despite the significant progress made in both research and …
Multi-behavior sequential transformer recommender
In most real-world recommender systems, users interact with items in a sequential and multi-
behavioral manner. Exploring the fine-grained relationship of items behind the users' multi …
behavioral manner. Exploring the fine-grained relationship of items behind the users' multi …
Generalization analysis of message passing neural networks on large random graphs
Message passing neural networks (MPNN) have seen a steep rise in popularity since their
introduction as generalizations of convolutional neural networks to graph-structured data …
introduction as generalizations of convolutional neural networks to graph-structured data …
A deep reinforcement learning framework based on an attention mechanism and disjunctive graph embedding for the job-shop scheduling problem
R Chen, W Li, H Yang - IEEE Transactions on Industrial …, 2022 - ieeexplore.ieee.org
The job-shop scheduling problem (JSSP) is a classical NP-hard combinatorial optimization
problem, and the operating efficiency of manufacturing system is affected directly by the …
problem, and the operating efficiency of manufacturing system is affected directly by the …
Fedrecattack: Model poisoning attack to federated recommendation
D Rong, S Ye, R Zhao, HN Yuen… - 2022 IEEE 38th …, 2022 - ieeexplore.ieee.org
Federated Recommendation (FR) has received con-siderable popularity and attention in the
past few years. In FR, for each user, its feature vector and interaction data are kept locally on …
past few years. In FR, for each user, its feature vector and interaction data are kept locally on …