H
Hossein A. Rahmani
Researcher at University of Zanjan
Publications - 27
Citations - 263
Hossein A. Rahmani is an academic researcher from University of Zanjan. The author has contributed to research in topics: Computer science & Recommender system. The author has an hindex of 5, co-authored 11 publications receiving 76 citations. Previous affiliations of Hossein A. Rahmani include University of Lugano.
Papers
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Book ChapterDOI
LGLMF: Local Geographical Based Logistic Matrix Factorization Model for POI Recommendation
Hossein A. Rahmani,Mohammad Aliannejadi,Sajad Ahmadian,Mitra Baratchi,Mohsen Afsharchi,Fabio Crestani +5 more
TL;DR: An effective geographical model is proposed by considering the user's main region of activity and the relevance of each location within that region and is fused into the Logistic Matrix Factorization to improve the accuracy of POI recommendation.
Book ChapterDOI
Joint Geographical and Temporal Modeling based on Matrix Factorization for Point-of-Interest Recommendation
TL;DR: Zhang et al. as discussed by the authors proposed a spatio-temporal activity-centers algorithm to model users' behavior more accurately by incorporating contextual information such as geographical and temporal influences to improve POI recommendation by addressing the data sparsity problem.
Proceedings ArticleDOI
Category-Aware Location Embedding for Point-of-Interest Recommendation
Hossein A. Rahmani,Mohammad Aliannejadi,Rasoul Mirzaei Zadeh,Mitra Baratchi,Mohsen Afsharchi,Fabio Crestani +5 more
TL;DR: In this article, a check-in module and a category module are proposed to capture the geographical influence of POIs derived from the sequence of users' check-ins, while the category module captures the characteristics of POI derived from category information.
Proceedings ArticleDOI
CPFair: Personalized Consumer and Producer Fairness Re-ranking for Recommender Systems
TL;DR: This work presents an optimization-based re-ranking approach that seamlessly integrates fairness constraints from both the consumer and producer-side in a joint objective framework, and demonstrates the role algorithms may play in minimizing data biases.
Book ChapterDOI
The Unfairness of Popularity Bias in Book Recommendation
TL;DR: In this article , the authors examine the first point of view in the book domain, and define three user groups based on their tendency towards popular items (i.e., Niche, Diverse, Bestseller-focused).