Fairness in graph mining: A survey
Graph mining algorithms have been playing a significant role in myriad fields over the years.
However, despite their promising performance on various graph analytical tasks, most of …
However, despite their promising performance on various graph analytical tasks, most of …
[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 …
[HTML][HTML] 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 …
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 …
[HTML][HTML] A comparative analysis of bias amplification in graph neural network approaches for recommender systems
Recommender Systems (RSs) are used to provide users with personalized item
recommendations and help them overcome the problem of information overload. Currently …
recommendations and help them overcome the problem of information overload. Currently …
[HTML][HTML] Bias assessment approaches for addressing user-centered fairness in GNN-based recommender systems
In today's technology-driven society, many decisions are made based on the results
provided by machine learning algorithms. It is widely known that the models generated by …
provided by machine learning algorithms. It is widely known that the models generated by …
Enhancing user and item representation with collaborative signals for KG-based recommendation
Y Zhang, X Gu - Neural Computing and Applications, 2024 - Springer
Abstract Knowledge graph (KG) shows great potential in improving recommendation
systems. Recent studies have focused on developing end-to-end models based on graph …
systems. Recent studies have focused on developing end-to-end models based on graph …
Beyond fixed time and space: next POI recommendation via multi-grained context and correlation
POI recommendation is significant for discovering attractive locations, crime prediction, and
smart city construction. Most existing methods only consider the fixed time and space …
smart city construction. Most existing methods only consider the fixed time and space …
Search and Society: Reimagining Information Access for Radical Futures
B Mitra - arXiv preprint arXiv:2403.17901, 2024 - arxiv.org
Information retrieval (IR) technologies and research are undergoing transformative changes.
It is our perspective that the community should accept this opportunity to re-center our …
It is our perspective that the community should accept this opportunity to re-center our …
Towards platform profit-aware fairness in personalized recommendation
S Liu, J Sun, X Deng, H Wang, W Liu, C Zhu… - International Journal of …, 2024 - Springer
The remarkable progress of machine learning has had a significant impact on decision-
making, thus fairness is an important topic. Existing fair recommendation methods generally …
making, thus fairness is an important topic. Existing fair recommendation methods generally …