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Naomi (Naime) Ranjbar Kermany
Naomi (Naime) Ranjbar Kermany
Commonwealth Bank of Australia, PhD from Macquarie university
Bestätigte E-Mail-Adresse bei hdr.mq.edu.au - Startseite
Titel
Zitiert von
Zitiert von
Jahr
A hybrid multi-criteria recommender system using ontology and neuro-fuzzy techniques
NR Kermany, SH Alizadeh
Electronic Commerce Research and Applications 21, 50-64, 2017
1202017
A fairness-aware multi-stakeholder recommender system
N Ranjbar Kermany, W Zhao, J Yang, J Wu, L Pizzato
World Wide Web 24 (6), 1995-2018, 2021
162021
An ethical multi-stakeholder recommender system based on evolutionary multi-objective optimization
NR Kermany, W Zhao, J Yang, J Wu, L Pizzato
2020 IEEE international conference on services computing (SCC), 478-480, 2020
152020
Fair-SRS: a fair session-based recommendation system
N Ranjbar Kermany, J Yang, J Wu, L Pizzato
Proceedings of the Fifteenth ACM International Conference on Web Search and …, 2022
122022
Reincre: Enhancing collaborative filtering recommendations by incorporating user rating credibility
NR Kermany, W Zhao, J Yang, J Wu
International Conference on Web Information Systems Engineering, 64-72, 2020
82020
A New Model for Best Customer Segment Selection Using Fuzzy TOPSIS Based on Shannon Entropy
N Ranjbar Kermany, S H Alizadeh
Journal of Computer & Robotics 5 (2), 7-12, 2012
42012
A fuzzy recommender system for forecasting customer segmentation by multi-variable fuzzy rule interpolation
NR Kermany, SH Alizadeh
2013 13th Iranian Conference on Fuzzy Systems (IFSC), 1-5, 2013
32013
A multi-stakeholder recommender system for rewards recommendations
N Ranjbar Kermany, L Pizzato, T Min, C Scott, A Leontjeva
Proceedings of the 16th ACM Conference on Recommender Systems, 484-487, 2022
22022
Incorporating user rating credibility in recommender systems
NR Kermany, W Zhao, T Batsuuri, J Yang, J Wu
Future Generation Computer Systems 147, 30-43, 2023
12023
Towards Fairness-aware Multi-Objective Recommendation Systems
NR Kermany
Macquarie University, 2024
2024
PD-SRS: Personalized Diversity for a Fair Session-Based Recommendation System
NR Kermany, L Pizzato, J Yang, S Xue, J Wu
International Conference on Service-Oriented Computing, 331-339, 2022
2022
Incorporating User Rating Credibility in Recommender Systems
N Ranjbar Kermany, W Zhao, J Yang, J Wu
Available at SSRN 4165425, 0
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