no code implementations • 22 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.
1 code implementation • 23 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.
1 code implementation • 17 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.
1 code implementation • 17 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.
1 code implementation • 17 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.
1 code implementation • 27 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.
1 code implementation • 27 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.
no code implementations • 20 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.
1 code implementation • 10 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.
no code implementations • 18 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.
no code implementations • 5 Apr 2020 • Mahdi Dehghan, Hossein A. Rahmani, Ahmad Ali Abin, Viet-Vu Vu
An efficient solution to cope with this concern is to hire T-shaped experts that are cost-effective.
1 code implementation • 24 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.
1 code implementation • 14 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.
no code implementations • 31 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.