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A survey on popularity bias in recommender systems
Recommender systems help people find relevant content in a personalized way. One main
promise of such systems is that they are able to increase the visibility of items in the long tail …
promise of such systems is that they are able to increase the visibility of items in the long tail …
[PDF][PDF] A survey on popularity bias in recommender systems
Recommender systems help people find relevant content in a personalized way. One main
promise of such systems is that they are able to increase the visibility of items in the long tail …
promise of such systems is that they are able to increase the visibility of items in the long tail …
Estimating propensity for causality-based recommendation without exposure data
Causality-based recommendation systems focus on the causal effects of user-item
interactions resulting from item exposure (ie, which items are recommended or exposed to …
interactions resulting from item exposure (ie, which items are recommended or exposed to …
Unbiased, Effective, and Efficient Distillation from Heterogeneous Models for Recommender Systems
In recent years, recommender systems have achieved remarkable performance by using
ensembles of heterogeneous models. However, this approach is costly due to the resources …
ensembles of heterogeneous models. However, this approach is costly due to the resources …
Theoretical and Empirical Insights into the Origins of Degree Bias in Graph Neural Networks
Graph Neural Networks (GNNs) often perform better for high-degree nodes than low-degree
nodes on node classification tasks. This degree bias can reinforce social marginalization by …
nodes on node classification tasks. This degree bias can reinforce social marginalization by …
Social Perception with Graph Attention Network for Recommendation
Recommendation systems are designed to uncover users' potential preferences and make
recommendations. However, they often face challenges such as data sparsity and the cold …
recommendations. However, they often face challenges such as data sparsity and the cold …
NeutronSketch: An in-depth exploration of redundancy in large-scale graph neural network training
Abstract Graph Neural Networks (GNNs) have achieved notable success in various
applications. However, the increasing scale of real-world graphs poses a challenge for …
applications. However, the increasing scale of real-world graphs poses a challenge for …
KGIE: Knowledge graph convolutional network for recommender system with interactive embedding
M Li, W Ma, Z Chu - Knowledge-Based Systems, 2024 - Elsevier
In recent years, knowledge graphs (KGs) have gained considerable traction across various
domains, especially in the realm of recommender systems, where their integration has …
domains, especially in the realm of recommender systems, where their integration has …
Decoupled Variational Graph Autoencoder for Link Prediction
YS Cho - Proceedings of the ACM on Web Conference 2024, 2024 - dl.acm.org
Link prediction is an important learning task for graph-structured data, and has become
increasingly popular due to its wide application areas. Graph Neural Network (GNN)-based …
increasingly popular due to its wide application areas. Graph Neural Network (GNN)-based …
Introduction to the Special Issue on Causal Inference for Recommender Systems
A significant proportion of machine learning methodologies for recommendation systems are
grounded in the fundamental principle of matching, utilizing perceptual and similarity-based …
grounded in the fundamental principle of matching, utilizing perceptual and similarity-based …