Fairsna: Algorithmic fairness in social network analysis

A Saxena, G Fletcher, M Pechenizkiy - ACM Computing Surveys, 2024 - dl.acm.org
In recent years, designing fairness-aware methods has received much attention in various
domains, including machine learning, natural language processing, and information …

[HTML][HTML] Efficient Visual-Aware Fashion Recommendation Using Compressed Node Features and Graph-Based Learning

US Malhi, J Zhou, A Rasool, S Siddeeq - Machine Learning and …, 2024 - mdpi.com
In fashion e-commerce, predicting item compatibility using visual features remains a
significant challenge. Current recommendation systems often struggle to incorporate high …

Does change agent selection procedure matter? A strategic decision-making toward a more objective selection approach

A Mashhady, H Khalili, A Sameti - Leadership & Organization …, 2022 - emerald.com
Purpose While studies have established the important role of change agents throughout
organizational change programs and emphasized the change agents' impact on outcomes …

[PDF][PDF] Fairness in Group Recommender Systems Using Variational Autoencoders

MS Ali, K Stefanidis - 2024 - trepo.tuni.fi
Recommender systems are integral to enhancing user experiences on platforms like
Amazon and Netflix by providing personalized suggestions. However, these systems often …

Context-Aware Music Recommendation Algorithm Combining Classification and Collaborative Filtering

X Wu, G Sun - Scalable Computing: Practice and Experience, 2024 - scpe.org
As an effective solution to the problem of information overload, personalized
recommendations have received widespread attention in the music field. A context-aware …

Fairness Identification of Large Language Models in Recommendation

W Liu, B Liu, J Qin, X Zhang, W Huang, Y Wang - 2024 - researchsquare.com
Ensuring fairness in recommendation systems necessitates that models do not discriminate
against users based on demographic information such as gender and age. Current fairness …

[PDF][PDF] Counterfactual Explanations for Group Recommendations

F Mubasher - 2024 - trepo.tuni.fi
This study tackles the challenge of generating counterfactual explanations in group
recommender systems by identifying a minimal set of items whose removal excludes a target …

[PDF][PDF] Fairness in Negative Influence Minimization in Social Networks

D Unnikrishnan - pure.tue.nl
The proliferation of fake news and misinformation campaigns in social networks has
emerged as a pressing concern, inflicting substantial harm on the public at large. Truth …

[PDF][PDF] Multi-Attribute Bias Mitigation in Recommender Systems

U Ahmed, K Stefanidis - homepages.tuni.fi
Variational Autoencoder (VAE) based recommender systems have successfully matched
users with potentially relevant items. VAEs work on the assumption that similar user profiles …

[PDF][PDF] Hybrid job recommendation model based on professional profile using data from job boards and Machine Learning libraries

A HUAMÁN, G REBAZA, D SUBAUSTE - iiis.org
This scientific article presents that the ideal job is crucial for undergraduate software
engineering students because it is related to their mental and financial health. The proposal …