Guest Editors
Associate Professor Jaekwang Kim
Email: linux@skku.edu
Affiliation: College of Computing and Informatics, Sungkyunkwan University, Suwon, 16419, Republic of Korea
Homepage:
Research Interests: artificial intelligence; recommendation & prediction system; intelligent system; machine intelligence & learning; pattern analysis; medical intelligence system
Summary
This Special Issue seeks to present the latest research, innovations, and applications in the fields of recommended systems and collaborative filtering, specifically focusing on their potential to transform decision-making processes across industries. With the exponential growth of data and the increasing demand for personalized experiences, collaborative filtering has become a cornerstone in recommendation algorithms, enabling more precise and user-tailored solutions. The Special Issue invites contributions that explore advanced methodologies, novel algorithms, real-world applications, and emerging trends in the domain of recommendation systems, with a special emphasis on intelligent and context-aware decision support systems.
The scope of this issue includes, but is not limited to:
· Collaborative Filtering Techniques: Exploration of traditional, hybrid, and deep learning-based approaches for collaborative filtering.
· Personalization and Context-Awareness: Enhancements to recommendation quality through contextual, temporal, and socio-demographic factors.
· Big Data and Scalability: Addressing the challenges of scalability in collaborative filtering algorithms with large-scale datasets.
· Evaluation Metrics and Benchmarking: Novel evaluation frameworks for measuring the accuracy and robustness of recommendation systems.
· Cross-Domain and Multimodal Recommendations: Bridging gaps between domains, and incorporating multimodal data (e.g., text, image, and video).
· Ethics, Fairness, and Privacy: Addressing the ethical implications and privacy concerns surrounding recommendation systems.
· Group Recommendations: Techniques for generating recommendations for groups of users, considering group dynamics, preferences, and consensus-building.
· LLM-Powered Recommendations: The integration of large language models (LLMs) in recommendation systems, leveraging advanced natural language processing capabilities for more accurate, context-sensitive, and personalized recommendations.
Keywords
recommended systems, collaborative filtering, personalization, context-aware systems, big data, deep learning, evaluation metrics, cross-domain recommendations, multimodal data, privacy and ethics, group recommendations, large language models, LLM-powered recommendations
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