Assistant Professor · Division of Computer Engineering (Big Data Track)
Hansung University

Researching personalized AI systems that understand users, integrate multimodal signals,
generate adaptive experiences, and interact through intelligent agents.

I am an Assistant Professor in the Division of Computer Engineering (Big Data Track) at Hansung University. My research focuses on personalized artificial intelligence, with particular interests in recommender systems, multimodal intelligence, large language models, review understanding, and AI agents.

At PRISM Lab, I study how user preferences, behavioral context, language, images, and external knowledge can be combined to build adaptive and trustworthy AI systems. My work spans the full personalized AI pipeline—from understanding users and predicting relevance to generating personalized content and enabling intelligent interaction.

• Personalized Recommender Systems
  User modeling, sequential and graph-based recommendation, context-aware personalization, and explainable recommendation

• Multimodal Intelligence
  Joint modeling of text, images, reviews, product information, and behavioral signals for recommendation and decision support

• Generative AI & Foundation Models
  Large language models, generative recommendation, efficient model adaptation, personalized generation, and review understanding

• Agentic Personalized AI
  Retrieval-augmented generation, conversational recommendation, memory, reasoning, and adaptive AI agents

• Trustworthy Review Intelligence
  Review helpfulness prediction, fake review detection, information consistency, and reliable e-commerce intelligence

• Ph.D. in Engineering, Big Data Analytics, Kyung Hee University, 2024
• M.S. in Engineering, Big Data Analytics, Kyung Hee University, 2021
• B.B.A. in Business Administration, Kyung Hee University, 2019

1. Lim, H., Park, S., Li, Q., Li, X., & Kim, J. (2026). Enhancing e-commerce recommendations through review summarization and multi-embedding feature fusion. Expert Systems, 43(9), e70390. DOI

2. Li, X., Li, Q., & Kim, J. (2026). Instruction-tuned large language models for review helpfulness prediction: An efficient fine-tuning framework for e-commerce review understanding. Expert Systems, 43(3), e70208. DOI

3. Lim, H., Park, S., Li, Q., Li, X., & Kim, J. (2026). What makes a review helpful? A multimodal prediction model in e-commerce. Electronic Commerce Research and Applications, 76, 101586. DOI

4. Lim, H., Li, X., Park, S., Li, Q., & Kim, J. (2026). Reducing contextual noise in review-based recommendation via aspect term extraction and attention modeling. Information Sciences, 735, 123078. DOI

5. Li, X., Li, Q., Ryu, D., & Kim, J. (2025). A BERT-based review helpfulness prediction model utilizing consistency of ratings and texts. Applied Intelligence, 55(7), 455. DOI

For a complete and current publication list, please visit my Google Scholar profile.

• Best Paper Award, Korean Intelligent Information Systems Society Spring Conference, 2026
• Excellent Paper Award, Korean Intelligent Information Systems Society Spring Conference, 2026
• Best Paper Award, Korea Knowledge Management Society Fall Conference, 2025
• Excellent Paper Award, Korean Operations Research and Management Science Society Fall Conference, 2025
• Best Paper Award, Korean Intelligent Information Systems Society Spring Conference, 2025
• Best Paper Award, Korean Intelligent Information Systems Society Fall Conference, 2024
• Outstanding Paper, Emerald Literati Awards, 2024
• Excellent Paper Award (two papers), Korean Operations Research and Management Science Society Fall Conference, 2023
• Best Paper Award, Korean IT Service Society Spring Conference, 2023
• Excellent Paper Award, Korean Intelligent Information Systems Society Spring Conference, 2021

• Email: leecy@hansung.ac.kr
• Office: Research Building 201, Hansung University
• Lab: PRISM Lab — Personalized AI Systems Lab

Last updated: August 2026