Advanced Models and Algorithms for Recommender Systems
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".
Deadline for manuscript submissions: 20 June 2025 | Viewed by 655
Special Issue Editor
Interests: artificial intelligence; recommender systems; machine learning; information retrieval; data science; intelligent systems; algorithms; databases; large language models
Special Issue Information
Dear Colleagues,
As digital ecosystems grow and diversify, recommender systems have become integral in delivering personalized experiences, enhancing user engagement across sectors like e-commerce, social media, entertainment, and healthcare. With the rapid advancement of AI and data science, there is a growing interest in developing models and algorithms that not only improve recommendation accuracy but also address challenges in scalability, interpretability, user privacy, ethics, cold-start problems, data heterogeneity, and the integration of large language models.
Traditional recommendation approaches, though widely used, face limitations in handling sparse, noisy, and evolving data environments. Recently, advanced techniques in deep learning, graph neural networks, and hybrid collaborative methods have shown promising potential in tackling these challenges by leveraging contextual, sequential, and multi-modal data.
This Special Issue, “Advanced Models and Algorithms for Recommender Systems”, brings together cutting-edge research focused on refining recommender systems through enhanced modeling, innovative algorithms, and practical implementations. Our aim is to provide a comprehensive resource for researchers and practitioners seeking to develop intelligent and reliable recommender systems in increasingly complex digital ecosystems.
Dr. Pavlos Kefalas
Guest Editor
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Keywords
- recommender systems
- personalization
- scalability
- interpretability
- user privacy
- cold-start problem
- data heterogeneity
- large language models in recommendations
- deep learning in recommendations
- graph neural networks
- hybrid collaborative filtering
- context-aware recommendations
- sequential recommendations
- multi-modal data integration
- fairness and transparency in recommendations
- real-world applications
- artificial intelligence
- machine learning
- data science
- information retrieval
- algorithms
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