12 citations · 38 across the 11 of their papers we have counts for
9 papers · 1 filter
Clarifying the Path to User Satisfaction: An Investigation into Clarification Usefulness
Hossein A. Rahmani, Xi Wang, Mohammad Aliannejadi +2
Clarifying questions are an integral component of modern information retrieval systems, directly impacting user satisfaction and overall system performance. Poorly formulated quest…
Provider Fairness and Beyond-Accuracy Trade-offs in Recommender Systems
Saeedeh Karimi, Hossein A. Rahmani, Mohammadmehdi Naghiaei +1
Recommender systems, while transformative in online user experiences, have raised concerns over potential provider-side fairness issues. These systems may inadvertently favor popul…
CAPRI: Context-Aware Interpretable Point-of-Interest Recommendation Framework
Ali Tourani, Hossein A. Rahmani, Mohammadmehdi Naghiaei +1
Point-of-Interest (POI ) recommendation systems have gained popularity for their unique ability to suggest geographical destinations with the incorporation of contextual informatio…
Towards Confidence-aware Calibrated Recommendation
Mohammadmehdi Naghiaei, Hossein A. Rahmani, Mohammad Aliannejadi +1
Recommender systems utilize users' historical data to learn and predict their future interests, providing them with suggestions tailored to their tastes. Calibration ensures that t…
Exploring the Impact of Temporal Bias in Point-of-Interest Recommendation
Hossein A. Rahmani, Mohammadmehdi Naghiaei, Ali Tourani +1
Recommending appropriate travel destinations to consumers based on contextual information such as their check-in time and location is a primary objective of Point-of-Interest (POI)…
Experiments on Generalizability of User-Oriented Fairness in Recommender Systems
Hossein A. Rahmani, Mohammadmehdi Naghiaei, Mahdi Dehghan +1
Recent work in recommender systems mainly focuses on fairness in recommendations as an important aspect of measuring recommendations quality. A fairness-aware recommender system ai…
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 Popularity Bias in Book Recommendation
Mohammadmehdi Naghiaei, Hossein A. Rahmani, Mahdi Dehghan
Recent studies have shown that recommendation systems commonly suffer from popularity bias. Popularity bias refers to the problem that popular items (i.e., frequently rated items)…
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…