28 citations · 78 across the 23 of their papers we have counts for
23 papers
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…
A Personalized Framework for Consumer and Producer Group Fairness Optimization in Recommender Systems
Hossein A. Rahmani, Mohammadmehdi Naghiaei, Yashar Deldjoo
In recent years, there has been an increasing recognition that when machine learning (ML) algorithms are used to automate decisions, they may mistreat individuals or groups, with l…
Improving Conversational Recommendation Systems via Bias Analysis and Language-Model-Enhanced Data Augmentation
Xi Wang, Hossein A. Rahmani, Jiqun Liu +1
Conversational Recommendation System (CRS) is a rapidly growing research area that has gained significant attention alongside advancements in language modelling techniques. However…
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…
A Survey on Asking Clarification Questions Datasets in Conversational Systems
Hossein A. Rahmani, Xi Wang, Yue Feng +3
The ability to understand a user's underlying needs is critical for conversational systems, especially with limited input from users in a conversation. Thus, in such a domain, Aski…
Towards Asking Clarification Questions for Information Seeking on Task-Oriented Dialogues
Yue Feng, Hossein A. Rahmani, Aldo Lipani +1
Task-oriented dialogue systems aim at providing users with task-specific services. Users of such systems often do not know all the information about the task they are trying to acc…
When and What to Ask Through World States and Text Instructions: IGLU NLP Challenge Solution
Zhengxiang Shi, Jerome Ramos, To Eun Kim +3
In collaborative tasks, effective communication is crucial for achieving joint goals. One such task is collaborative building where builders must communicate with each other to con…
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)…
ViralBERT: A User Focused BERT-Based Approach to Virality Prediction
Rikaz Rameez, Hossein A. Rahmani, Emine Yilmaz
Recently, Twitter has become the social network of choice for sharing and spreading information to a multitude of users through posts called 'tweets'. Users can easily re-share the…
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…
A Systematic Analysis on the Impact of Contextual Information on Point-of-Interest Recommendation
Hossein A. Rahmani, Mohammad Aliannejadi, Mitra Baratchi +1
As the popularity of Location-based Social Networks (LBSNs) increases, designing accurate models for Point-of-Interest (POI) recommendation receives more attention. POI recommendat…
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…
Demographic Biases of Crowd Workers in Key Opinion Leaders Finding
Hossein A. Rahmani, Jie Yang
Key Opinion Leaders (KOLs) are people that have a strong influence and their opinions are listened to by people when making important decisions. Crowdsourcing provides an efficient…
Mining Shape of Expertise: A Novel Approach Based on Convolutional Neural Network
Mahdi Dehghan, Hossein A. Rahmani, Ahmad Ali Abin +1
Expert finding addresses the task of retrieving and ranking talented people on the subject of user query. It is a practical issue in the Community Question Answering networks. Recr…
Joint Geographical and Temporal Modeling based on Matrix Factorization for Point-of-Interest Recommendation
Hossein A. Rahmani, Mohammad Aliannejadi, Mitra Baratchi +1
With the popularity of Location-based Social Networks, Point-of-Interest (POI) recommendation has become an important task, which learns the users' preferences and mobility pattern…
LGLMF: Local Geographical based Logistic Matrix Factorization Model for POI Recommendation
Hossein A. Rahmani, Mohammad Aliannejadi, Sajad Ahmadian +3
With the rapid growth of Location-Based Social Networks, personalized Points of Interest (POIs) recommendation has become a critical task to help users explore their surroundings.…
Category-Aware Location Embedding for Point-of-Interest Recommendation
Hossein A. Rahmani, Mohammad Aliannejadi, Rasoul Mirzaei Zadeh +3
Recently, Point of interest (POI) recommendation has gained ever-increasing importance in various Location-Based Social Networks (LBSNs). With the recent advances of neural models,…
Comparison of Tumor and Normal Cells Protein-Protein Interaction Network Parameters
Hossein A. Rahmani, AliReza Khanteymoori, Mohammad Olyaee
In this paper, we compared cancerous and normal cell according to their protein-protein interaction network. Cancer is one of the complicated diseases and experimental investigatio…