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 …
An insight into topological, machine and Deep Learning-based approaches for influential node identification in social media networks: a systematic review
Online social networks are social interaction platforms having dynamic nature with billions of
users around the world. Online social communications among its multiple users cause a …
users around the world. Online social communications among its multiple users cause a …
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 …
Overcoming diverse undesired effects in recommender systems: A deontological approach
In today's digital landscape, recommender systems have gained ubiquity as a means of
directing users toward personalized products, services, and content. However, despite their …
directing users toward personalized products, services, and content. However, despite their …
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 …
Providing Previously Unseen Users Fair Recommendations Using Variational Autoencoders
B Vassøy, H Langseth, B Kille - … of the 17th ACM Conference on …, 2023 - dl.acm.org
An emerging definition of fairness in machine learning requires that models are oblivious to
demographic user information, eg, a user's gender or age should not influence the model …
demographic user information, eg, a user's gender or age should not influence the model …
The Effect of Similarity Metric and Group Size on Outlier Selection & Satisfaction in Group Recommender Systems
P Dokoupil, L Peska - Adjunct Proceedings of the 31st ACM Conference …, 2023 - dl.acm.org
Group recommender systems (GRS) are a specific case of recommender systems (RS),
where recommendations are constructed to a group of users rather than an individual. GRS …
where recommendations are constructed to a group of users rather than an individual. GRS …
Explaining Recommendation Fairness from a User/Item Perspective
Recommender systems play a crucial role in personalizing user experiences, yet ensuring
fairness in their outcomes remains an elusive challenge. This work explores the impact of …
fairness in their outcomes remains an elusive challenge. This work explores the impact of …
Correcting for Popularity Bias in Recommender Systems via Item Loss Equalization
J Prent, M Mansoury - arXiv preprint arXiv:2410.04830, 2024 - arxiv.org
Recommender Systems (RS) often suffer from popularity bias, where a small set of popular
items dominate the recommendation results due to their high interaction rates, leaving many …
items dominate the recommendation results due to their high interaction rates, leaving many …
The Fault in Our Recommendations: On the Perils of Optimizing the Measurable
Recommendation systems are widespread, and through customized recommendations,
promise to match users with options they will like. To that end, data on engagement is …
promise to match users with options they will like. To that end, data on engagement is …