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 …
Fairness in music recommender systems: A stakeholder-centered mini review
K Dinnissen, C Bauer - Frontiers in big Data, 2022 - frontiersin.org
The performance of recommender systems highly impacts both music streaming platform
users and the artists providing music. As fairness is a fundamental value of human life, there …
users and the artists providing music. As fairness is a fundamental value of human life, there …
Joint multisided exposure fairness for recommendation
Prior research on exposure fairness in the context of recommender systems has focused
mostly on disparities in the exposure of individual or groups of items to individual users of …
mostly on disparities in the exposure of individual or groups of items to individual users of …
Experiments on generalizability of user-oriented fairness in recommender systems
Recent work in recommender systems mainly focuses on fairness in recommendations as
an important aspect of measuring recommendations quality. A fairness-aware recommender …
an important aspect of measuring recommendations quality. A fairness-aware recommender …
A multi-objective optimization framework for multi-stakeholder fairness-aware recommendation
Nowadays, most online services are hosted on multi-stakeholder marketplaces, where
consumers and producers may have different objectives. Conventional recommendation …
consumers and producers may have different objectives. Conventional recommendation …
General intelligence requires rethinking exploration
M Jiang, T Rocktäschel… - Royal Society Open …, 2023 - royalsocietypublishing.org
We are at the cusp of a transition from 'learning from data'to 'learning what data to learn
from'as a central focus of artificial intelligence (AI) research. While the first-order learning …
from'as a central focus of artificial intelligence (AI) research. While the first-order learning …
[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 …
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 …
Algorithms are not neutral: Bias in collaborative filtering
C Stinson - AI and Ethics, 2022 - Springer
Abstract When Artificial Intelligence (AI) is applied in decision-making that affects people's
lives, it is now well established that the outcomes can be biased or discriminatory. The …
lives, it is now well established that the outcomes can be biased or discriminatory. The …
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 …