[HTML][HTML] A survey on fairness-aware recommender systems
As information filtering services, recommender systems have extremely enriched our daily
life by providing personalized suggestions and facilitating people in decision-making, which …
life by providing personalized suggestions and facilitating people in decision-making, which …
Recent developments in recommender systems: A survey
In this technical survey, the latest advancements in the field of recommender systems are
comprehensively summarized. The objective of this study is to provide an overview of the …
comprehensively summarized. The objective of this study is to provide an overview of the …
Beyond-accuracy: a review on diversity, serendipity, and fairness in recommender systems based on graph neural networks
By providing personalized suggestions to users, recommender systems have become
essential to numerous online platforms. Collaborative filtering, particularly graph-based …
essential to numerous online platforms. Collaborative filtering, particularly graph-based …
Sdgnn: Symmetry-preserving dual-stream graph neural networks
Dear Editor, This letter proposes a symmetry-preserving dual-stream graph neural network
(SDGNN) for precise representation learning to an undirected weighted graph (UWG) …
(SDGNN) for precise representation learning to an undirected weighted graph (UWG) …
Dynamic graph evolution learning for recommendation
Graph neural network (GNN) based algorithms have achieved superior performance in
recommendation tasks due to their advanced capability of exploiting high-order connectivity …
recommendation tasks due to their advanced capability of exploiting high-order connectivity …
Adaptive popularity debiasing aggregator for graph collaborative filtering
The graph neural network-based collaborative filtering (CF) models user-item interactions as
a bipartite graph and performs iterative aggregation to enhance performance. Unfortunately …
a bipartite graph and performs iterative aggregation to enhance performance. Unfortunately …
Two-stream graph convolutional network-incorporated latent feature analysis
F Bi, T He, Y Xie, X Luo - IEEE Transactions on Services …, 2023 - ieeexplore.ieee.org
Historical Quality-of-Service (QoS) data describing existing user-service invocations are vital
to understanding user behaviors and cloud service conditions. Collaborative Filtering (CF) …
to understanding user behaviors and cloud service conditions. Collaborative Filtering (CF) …
Mitigating Recommendation Biases via Group-Alignment and Global-Uniformity in Representation Learning
Collaborative Filtering (CF) plays a crucial role in modern recommender systems, leveraging
historical user-item interactions to provide personalized suggestions. However, CF-based …
historical user-item interactions to provide personalized suggestions. However, CF-based …
A fast nonnegative autoencoder-based approach to latent feature analysis on high-dimensional and incomplete data
F Bi, T He, X Luo - IEEE Transactions on Services Computing, 2023 - ieeexplore.ieee.org
High-Dimensional and Incomplete (HDI) data are frequently encountered in various Big
Data-related applications. Despite its incompleteness, an HDI data repository contains rich …
Data-related applications. Despite its incompleteness, an HDI data repository contains rich …
FairGap: Fairness-aware recommendation via generating counterfactual graph
The emergence of Graph Neural Networks (GNNs) has greatly advanced the development
of recommendation systems. Recently, many researchers have leveraged GNN-based …
of recommendation systems. Recently, many researchers have leveraged GNN-based …