Special Session on Recommender Systems and Intelligent Applications (RSIA)

I. Objectives

In the era of information overload and intelligent digital ecosystems, Recommender Systems (RS) and personalized technologies have become core components of computational intelligence applications in engineering, e-commerce, mobility, education, and smart environments. Recent advancements in Graph Neural Networks (GNNs), Spatiotemporal Modeling, Generative AI, and Large Language Models (LLMs) are pushing the boundaries of personalization toward unprecedented levels of context-awareness, explainability, and efficiency. This Special Session aims to provide an international forum for researchers, engineers, and practitioners to share state-of-the-art developments, novel algorithms, and practical applications in recommender systems and personalized intelligence.

The primary objective of this Special Session is to gather original research contributions on theoretical advances and real-world engineering applications. It aims to highlight innovative approaches in graph-based learning, spatiotemporal context, and trustworthy systems, offering valuable insights into the generalizability and practical applicability of modern recommendation models.

II. Scope

We invite contributions covering topics including, but not limited to, the following:

• Graph-based Recommendation: Graph Neural Networks (GNNs), Knowledge Graphs, and Heterogeneous Information Networks for personalization.
• Sequential & Spatiotemporal RS: Trajectory modeling, context-aware recommendation, and location-based point-of-interest (POI) services.
• Generative AI & LLMs in Personalization: LLM-augmented recommendation, retrieval-augmented generation (RAG), and dynamic prompt engineering.
• Educational Data Mining & Adaptive Systems: Learning path recommendation, student performance analytics, and personalized learning environments.
• Trustworthy & Responsible RS: Explainability, fairness, bias mitigation, privacy preservation, and robustness.
• Practical Engineering & Industry Applications: Scalable recommendation pipelines, long-tail item coverage, cross-domain recommendation, and cold-start solutions.

III. Submission link: Click here

Please select the Track/Session: “Special Session on Recommender Systems and Intelligent Applications (RSIA)” during submission

IV. Session Organizers

• Nguyen Thai Nghe, Can Tho University, Vietnam. Email: ntnghe@cit.ctu.edu.vn
• Tran Trung Tin, Ton Duc Thang University, Ho Chi Minh City, Vietnam. Email: trantrungtin@tdtu.edu.vn
• Bui Ngoc Dung, University of Transport and Communications, Vietnam. Email: dnbui@utc.edu.vn
• Huynh Ngoc Tu, Ton Duc Thang University, Ho Chi Minh City, Vietnam. Email: huynhngoctu@tdtu.edu.vn
• Duong Huu Phuc, Ton Duc Thang University, Ho Chi Minh City, Vietnam. Email: duonghuuphuc@tdtu.edu.vn
• Mai Van Manh, Ton Duc Thang University, Ho Chi Minh City, Vietnam. Email: maivanmanh@tdtu.edu.vn
• Nguyen Quoc Thuan, Ho Chi Minh City Open University, Vietnam. Email: thuannq@ou.edu.vn
• Vinoth Nageshwaran, University of the Cumberlands, USA. Email: vnageshwaran41452@ucumberlands.edu