Guest Editors
Dr. Qinglong Li
Email: leecy@hansung.ac.kr
Affiliation: Division of Computer Engineering, Hansung University, Seoul, 02876, Republic of Korea
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Research Interests: recommender system, machine learning, deep learning, natural language process
Summary
With the exponential growth of data across domains such as e-commerce, media, healthcare, and education, the demand for personalized services has never been greater. Recommender systems play a pivotal role in tailoring user experiences by predicting preferences and delivering relevant content or services. Recent advances in deep learning, large language models (LLMs), and multimodal data integration have significantly enhanced the capacity and flexibility of recommender systems.
This Special Issue aims to bring together innovative research and practical developments in the field of intelligent personalized recommendation. In particular, we welcome contributions that explore how deep learning models, including transformer-based architectures and LLMs, can be leveraged to improve the performance, interpretability, and adaptability of recommender systems. Topics of interest include, but are not limited to:
· Collaborative filtering enhanced by deep learning
· Multimodal recommender systems integrating text, images, and structured data
· Applications of LLMs in user modeling and personalized recommendations
· Business analytics and explainable recommendation techniques
· Review-based and knowledge-aware recommendation
· Personalization in dynamic environments such as streaming platforms or smart devices
· Fairness, robustness, transparency, and data privacy in personalized services
This Special Issue welcomes both theoretical advancements and applied studies from academia and industry that contribute to the design, evaluation, and deployment of next-generation personalized recommender systems.
Keywords
Recommender systems, deep learning, large language models, multimodal learning, user modeling, business analytics, personalization, collaborative filtering, explainable AI, review analysis
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