Submission Deadline: 01 July 2025 View: 276 Submit to Special Issue
Prof. Jason J. Jung
Email: j3ung@cau.ac.kr
Affiliation: Department of Computer Engineering, Chung-Ang University, Seoul, 06974, South Korea
Research Interests: Recommendation service, Social network analytics, and Big data mining
Prof. Grzegorz Nalepa
Email: grzegorz.j.nalepa@uj.edu.pl
Affiliation: Faculty of Physics Astronomy and Applied Computer Science, Jagiellonian University, Kraków, 31-007, Poland
Research Interests: Knowledge-based systems, Affective computing, Context-aware systems, and Data mining
Prof. Paulo Salgado Gomes de Mattos Neto
Email: psgmn@cin.ufpe.br
Affiliation: Centro de Informática, Universidade Federal de Pernambuco, Recife, 50670-901, Brazil
Research Interests: Machine learning, Metaheuristic methods
In recent years, Generative Artificial Intelligence (AI) has emerged as a groundbreaking technology with transformative potential across various domains, including recommendation services. This special issue aims to explore the intersection of Generative AI and recommendation systems, bringing together novel methodologies, applications, and challenges to advance the state of the art.
This special issue will focus on the latest advancements, theoretical contributions, and practical applications of Generative AI in recommendation systems. We invite high-quality, original research and review articles that address, but are not limited to, the following topics:
Suggested themes
- Generative Models for Personalization:
· Application of generative adversarial networks (GANs), variational autoencoders (VAEs), and transformers in recommendation systems.
· Enhancing user-item interactions through generative approaches.
- Content Creation and Augmentation:
· Synthetic data generation for addressing data sparsity and cold-start problems.
· Generative AI for creating personalized content such as product descriptions, reviews, and media recommendations.
- Fairness, Ethics, and Trust:
· Ensuring bias mitigation and fairness in generative recommendation systems.
· Ethical implications and challenges of using generative models in user-centric services.
- Real-Time and Context-Aware Recommendations:
· Leveraging generative AI for dynamic, real-time recommendation scenarios.
· Contextual and multi-modal data fusion for more nuanced recommendations.
- Evaluation and Benchmarking:
· New metrics and frameworks for assessing the performance of generative recommendation systems.
· Benchmarks and datasets tailored for generative AI in recommendation.
- Applications and Case Studies:
· Success stories and challenges of deploying generative AI for recommendation in e-commerce, media, education, healthcare, and social networks.
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