Search Results for author: Hossein A. Rahmani

Found 24 papers, 18 papers with code

Understanding the Role of User Profile in the Personalization of Large Language Models

1 code implementation22 Jun 2024 Bin Wu, Zhengyan Shi, Hossein A. Rahmani, Varsha Ramineni, Emine Yilmaz

Utilizing user profiles to personalize Large Language Models (LLMs) has been shown to enhance the performance on a wide range of tasks.

Synthetic Test Collections for Retrieval Evaluation

1 code implementation13 May 2024 Hossein A. Rahmani, Nick Craswell, Emine Yilmaz, Bhaskar Mitra, Daniel Campos

Previous studies demonstrate that LLMs have the potential to generate synthetic relevance judgments for use in the evaluation of IR systems.

Information Retrieval Retrieval

Clarifying the Path to User Satisfaction: An Investigation into Clarification Usefulness

no code implementations2 Feb 2024 Hossein A. Rahmani, Xi Wang, Mohammad Aliannejadi, Mohammadmehdi Naghiaei, Emine Yilmaz

Clarifying questions are an integral component of modern information retrieval systems, directly impacting user satisfaction and overall system performance.

Information Retrieval

Improving Conversational Recommendation Systems via Bias Analysis and Language-Model-Enhanced Data Augmentation

1 code implementation25 Oct 2023 Xi Wang, Hossein A. Rahmani, Jiqun Liu, Emine Yilmaz

Conversational Recommendation System (CRS) is a rapidly growing research area that has gained significant attention alongside advancements in language modelling techniques.

Data Augmentation Language Modelling +2

Provider Fairness and Beyond-Accuracy Trade-offs in Recommender Systems

1 code implementation8 Sep 2023 Saeedeh Karimi, Hossein A. Rahmani, Mohammadmehdi Naghiaei, Leila Safari

Recommender systems, while transformative in online user experiences, have raised concerns over potential provider-side fairness issues.

Fairness Recommendation Systems +1

CAPRI: Context-Aware Interpretable Point-of-Interest Recommendation Framework

1 code implementation20 Jun 2023 Ali Tourani, Hossein A. Rahmani, Mohammadmehdi Naghiaei, Yashar Deldjoo

Point-of-Interest (POI ) recommendation systems have gained popularity for their unique ability to suggest geographical destinations with the incorporation of contextual information such as time, location, and user-item interaction.

Diversity Fairness +1

A Survey on Asking Clarification Questions Datasets in Conversational Systems

1 code implementation25 May 2023 Hossein A. Rahmani, Xi Wang, Yue Feng, Qiang Zhang, Emine Yilmaz, Aldo Lipani

The ability to understand a user's underlying needs is critical for conversational systems, especially with limited input from users in a conversation.

Towards Confidence-aware Calibrated Recommendation

no code implementations22 Aug 2022 Mohammadmehdi Naghiaei, Hossein A. Rahmani, Mohammad Aliannejadi, Nasim Sonboli

Calibration ensures that the distribution of recommended item categories is consistent with the user's historical data.

Diversity Recommendation Systems +1

Exploring the Impact of Temporal Bias in Point-of-Interest Recommendation

1 code implementation23 Jul 2022 Hossein A. Rahmani, Mohammadmehdi Naghiaei, Ali Tourani, Yashar Deldjoo

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) recommender systems.

Fairness Recommendation Systems

Experiments on Generalizability of User-Oriented Fairness in Recommender Systems

1 code implementation17 May 2022 Hossein A. Rahmani, Mohammadmehdi Naghiaei, Mahdi Dehghan, Mohammad Aliannejadi

In this paper, we re-produce a user-oriented fairness study and provide extensive experiments to analyze the dependency of their proposed method on various fairness and recommendation aspects, including the recommendation domain, nature of the base ranking model, and user grouping method.

Fairness Recommendation Systems +1

ViralBERT: A User Focused BERT-Based Approach to Virality Prediction

1 code implementation17 May 2022 Rikaz Rameez, Hossein A. Rahmani, Emine Yilmaz

We collect a dataset of 330k tweets to train ViralBERT and validate the efficacy of our model using baselines from current studies in this field.

Sentiment Analysis

CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systems

1 code implementation17 Apr 2022 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, or economic consequences.

Fairness Recommendation Systems +1

The Unfairness of Popularity Bias in Book Recommendation

1 code implementation27 Feb 2022 Mohammadmehdi Naghiaei, Hossein A. Rahmani, Mahdi Dehghan

Furthermore, our study shows a tradeoff between personalization and unfairness of popularity bias in recommendation algorithms for users belonging to the Diverse and Bestseller groups, that is, algorithms with high capability of personalization suffer from the unfairness of popularity bias.

Fairness Recommendation Systems

The Unfairness of Active Users and Popularity Bias in Point-of-Interest Recommendation

1 code implementation27 Feb 2022 Hossein A. Rahmani, Yashar Deldjoo, Ali Tourani, Mohammadmehdi Naghiaei

This paper studies the interplay between (i) the unfairness of active users, (ii) the unfairness of popular items, and (iii) the accuracy (personalization) of recommendation as three angles of our study triangle.

Fairness Recommendation Systems

A Systematic Analysis on the Impact of Contextual Information on Point-of-Interest Recommendation

no code implementations20 Jan 2022 Hossein A. Rahmani, Mohammad Aliannejadi, Mitra Baratchi, Fabio Crestani

The major contributions of this paper are: (i) providing an extensive survey of context-aware location recommendation (ii) quantifying and analyzing the impact of different contextual information (e. g., social, temporal, spatial, and categorical) in the POI recommendation on available baselines and two new linear and non-linear models, that can incorporate all the major contextual information into a single recommendation model, and (iii) evaluating the considered models using two well-known real-world datasets.

Leveraging Social Influence based on Users Activity Centers for Point-of-Interest Recommendation

1 code implementation10 Jan 2022 Kosar Seyedhoseinzadeh, Hossein A. Rahmani, Mohsen Afsharchi, Mohammad Aliannejadi

To this end, we model social influence based on two factors: similarities between users in terms of common check-ins and the friendships between them.

Recommendation Systems

Demographic Biases of Crowd Workers in Key Opinion Leaders Finding

no code implementations18 Oct 2021 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.

counterfactual

Joint Geographical and Temporal Modeling based on Matrix Factorization for Point-of-Interest Recommendation

1 code implementation24 Jan 2020 Hossein A. Rahmani, Mohammad Aliannejadi, Mitra Baratchi, Fabio Crestani

Previous studies show that incorporating contextual information such as geographical and temporal influences is necessary to improve POI recommendation by addressing the data sparsity problem.

LGLMF: Local Geographical based Logistic Matrix Factorization Model for POI Recommendation

1 code implementation14 Sep 2019 Hossein A. Rahmani, Mohammad Aliannejadi, Sajad Ahmadian, Mitra Baratchi, Mohsen Afsharchi, Fabio Crestani

To address these problems, a POI recommendation method is proposed in this paper based on a Local Geographical Model, which considers both users' and locations' points of view.

Category-Aware Location Embedding for Point-of-Interest Recommendation

no code implementations31 Jul 2019 Hossein A. Rahmani, Mohammad Aliannejadi, Rasoul Mirzaei Zadeh, Mitra Baratchi, Mohsen Afsharchi, Fabio Crestani

With the recent advances of neural models, much work has sought to leverage neural networks to learn neural embeddings in a pre-training phase that achieve an improved representation of POIs and consequently a better recommendation.

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