Fairness in recommender systems: research landscape and future directions

Y Deldjoo, D Jannach, A Bellogin, A Difonzo… - User Modeling and User …, 2024 - Springer
Recommender systems can strongly influence which information we see online, eg, on
social media, and thus impact our beliefs, decisions, and actions. At the same time, these …

Exploring gender biases in ML and AI academic research through systematic literature review

S Shrestha, S Das - Frontiers in artificial intelligence, 2022 - frontiersin.org
Automated systems that implement Machine learning (ML) and Artificial Intelligence (AI)
algorithms present promising solutions to a variety of technological and non-technological …

More than Machines: The Role of the Future Retail Salesperson in Enhancing the Customer Experience

A Pappas, E Fumagalli, M Rouziou, W Bolander - Journal of Retailing, 2023 - Elsevier
Retail sales has consistently faced threats by technology throughout history, with the recent
advent of Artificial Intelligence (AI) posing the most recent challenge. It is often said that …

Fairness in recommendation: A survey

Y Li, H Chen, S Xu, Y Ge, J Tan, S Liu… - arxiv preprint arxiv …, 2022 - arxiv.org
As one of the most pervasive applications of machine learning, recommender systems are
playing an important role on assisting human decision making. The satisfaction of users and …

[HTML][HTML] Explaining recommender systems fairness and accuracy through the lens of data characteristics

Y Deldjoo, A Bellogin, T Di Noia - Information processing & management, 2021 - Elsevier
The impact of data characteristics on the performance of classical recommender systems
has been recently investigated and produced fruitful results about the relationship they have …

Fairness in recommendation: Foundations, methods, and applications

Y Li, H Chen, S Xu, Y Ge, J Tan, S Liu… - ACM Transactions on …, 2023 - dl.acm.org
As one of the most pervasive applications of machine learning, recommender systems are
playing an important role on assisting human decision-making. The satisfaction of users and …

[PDF][PDF] Recommender systems under European AI regulations

T Di Noia, N Tintarev, P Fatourou… - Communications of the …, 2022 - dl.acm.org
Framework for AI), the EC aims at introducing the first comprehensive legal framework on AI,
which will identify specific risks for AI, provide a collection of high-risk application domains …

WECHSEL: Effective initialization of subword embeddings for cross-lingual transfer of monolingual language models

B Minixhofer, F Paischer, N Rekabsaz - arxiv preprint arxiv:2112.06598, 2021 - arxiv.org
Large pretrained language models (LMs) have become the central building block of many
NLP applications. Training these models requires ever more computational resources and …

Analyzing item popularity bias of music recommender systems: are different genders equally affected?

O Lesota, A Melchiorre, N Rekabsaz, S Brandl… - Proceedings of the 15th …, 2021 - dl.acm.org
Several studies have identified discrepancies between the popularity of items in user
profiles and the corresponding recommendation lists. Such behavior, which concerns a …

Improving recommendation fairness via data augmentation

L Chen, L Wu, K Zhang, R Hong, D Lian… - Proceedings of the …, 2023 - dl.acm.org
Collaborative filtering based recommendation learns users' preferences from all users'
historical behavior data, and has been popular to facilitate decision making. Recently, the …