Deep Generative Models and Recommender Systems
A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Artificial Intelligence".
Deadline for manuscript submissions: 15 February 2026 | Viewed by 8
Special Issue Editors
Interests: recommender system; large language models; information retrieval
Interests: information retrieval; recommender systems; graph networks
Interests: complexity science; time series analysis; complex network; data mining; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
Recommender systems play a critical role in delivering personalized services and content to users across various domains, including tourism, healthcare, education, and e-commerce. In recent years, the rapid development of deep generative models—such as diffusion models and large language models—has opened up new possibilities to enhance recommender systems beyond traditional approaches. These models significantly enhance the system’s ability to generate accurate, diverse, and personalized recommendations.
This Special Issue seeks to highlight recent theoretical and practical innovations that integrate ideas from generative AI and recommendation technologies. We invite original research articles, empirical studies, and comprehensive reviews that explore generative methods addressing longstanding and emerging challenges in the field of recommender systems. Research areas may include (but not limited to) the following:
- Conversational recommender systems empowered by generative models;
- Enhancing traditional recommender tasks using generative approaches;
- User and item representation learning with generative models;
- Trustworthy, fair, and unbiased recommendation using generative AI;
- Explainable recommendation with generative models;
- Privacy-preserving recommendation based on generative models;
- User feedback generation and simulation for recommender systems using generative models;
- Evaluation of recommender systems with generative models;
- Data generation and augmentation for recommender systems;
- Item retrieval and content generation using generative AI;
- Multimodal recommender systems leveraging generative models (text, images, audio, etc.);
- Domain-specific applications of generative model-enhanced recommender systems.
We look forward to receiving your contributions.
Prof. Dr. Jie Zou
Dr. Wanyu Chen
Dr. Shimin Cai
Guest Editors
Manuscript Submission Information
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Keywords
- recommender systems
- deep generative models
- large language models
- conversational recommender systems
- user and item modeling
- user simulation
- trustworthy recommendation
- explainable recommendation
- privacy-preserving recommendation
- multimodal recommendation
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