12 citations · 29 across the 11 of their papers we have counts for
9 papers · 1 filter
Towards Fair Rankings: Leveraging LLMs for Gender Bias Detection and Measurement
Maryam Mousavian, Zahra Abbasiantaeb, Mohammad Aliannejadi +1
The presence of social biases in Natural Language Processing (NLP) and Information Retrieval (IR) systems is an ongoing challenge, which underlines the importance of developing rob…
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…
Analyzing Coherency in Facet-based Clarification Prompt Generation for Search
Oleg Litvinov, Ivan Sekulić, Mohammad Aliannejadi +1
Clarifying user's information needs is an essential component of modern search systems. While most of the approaches for constructing clarifying prompts rely on query facets, the i…
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…
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…
Leveraging Social Influence based on Users Activity Centers for Point-of-Interest Recommendation
Kosar Seyedhoseinzadeh, Hossein A. Rahmani, Mohsen Afsharchi +1
Recommender Systems (RSs) aim to model and predict the user preference while interacting with items, such as Points of Interest (POIs). These systems face several challenges, such…