4 citations · 8 across the 5 of their papers we have counts for
14 papers
CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systems
Mohammadmehdi Naghiaei, Hossein A. Rahmani, Yashar Deldjoo
Recently, there has been a rising awareness that when machine learning (ML) algorithms are used to automate choices, they may treat/affect individuals unfairly, with legal, ethical…
The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendation
Hossein A. Rahmani, Yashar Deldjoo, Ali Tourani +1
Point-of-Interest (POI) recommender systems provide personalized recommendations to users and help businesses attract potential customers. Despite their success, recent studies sug…
Simulations for novel problems in recommendation: analyzing misinformation and data characteristics
Alejandro Bellogín, Yashar Deldjoo
In this position paper, we discuss recent applications of simulation approaches for recommender systems tasks. In particular, we describe how they were used to analyze the problem…
Understanding the Effects of Adversarial Personalized Ranking Optimization Method on Recommendation Quality
Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia +1
Recommender systems (RSs) employ user-item feedback, e.g., ratings, to match customers to personalized lists of products. Approaches to top-k recommendation mainly rely on Learning…
FedeRank: User Controlled Feedback with Federated Recommender Systems
Vito Walter Anelli, Yashar Deldjoo, Tommaso Di Noia +2
Recommender systems have shown to be a successful representative of how data availability can ease our everyday digital life. However, data privacy is one of the most prominent con…
Multi-Step Adversarial Perturbations on Recommender Systems Embeddings
Vito Walter Anelli, Alejandro Bellogín, Yashar Deldjoo +2
Recommender systems (RSs) have attained exceptional performance in learning users' preferences and helping them in finding the most suitable products. Recent advances in adversaria…