A hybrid multi-criteria recommender system using ontology and neuro-fuzzy techniques. - Naime Ranjbar Kermany; Sasan Hossein Alizadeh (Electron. Commer. Res. Appl., 2017)

ReInCre: Enhancing Collaborative Filtering Recommendations by Incorporating User Rating Credibility. - Naime Ranjbar Kermany; Weiliang Zhao; Jian Yang 001; Jia Wu 001 (WISE Workshops, 2019)

An Ethical Multi-Stakeholder Recommender System Based on Evolutionary Multi-Objective Optimization. - Naime Ranjbar Kermany; Weiliang Zhao; Jian Yang 001; Jia Wu 001; Luiz Pizzato (SCC, 2020)

A fairness-aware multi-stakeholder recommender system. - Naime Ranjbar Kermany; Weiliang Zhao; Jian Yang 001; Jia Wu 001; Luiz Pizzato (World Wide Web, 2021)

PD-SRS: Personalized Diversity for a Fair Session-Based Recommendation System. - Naime Ranjbar Kermany; Luiz Pizzato; Jian Yang 001; Shan Xue 001; Jia Wu 001 (ICSOC, 2022)

A Multi-Stakeholder Recommender System for Rewards Recommendations. - Naime Ranjbar Kermany; Luiz Pizzato; Thireindar Min; Callum Scott; Anna Leontjeva (RecSys, 2022)

Fair-SRS: A Fair Session-based Recommendation System. - Naime Ranjbar Kermany; Jian Yang 001; Jia Wu 001; Luiz Pizzato (WSDM, 2022)

Incorporating user rating credibility in recommender systems. - Naime Ranjbar Kermany; Weiliang Zhao; Tseesuren Batsuuri; Jian Yang 001; Jia Wu 001 (Future Gener. Comput. Syst., 2023)